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What's New in New Mexico
Superlatives & Rankings
Facts, Superlatives & Rankings
A visit to New Mexico is truly unlike anything you've experienced. Adventures steeped in culture start with the history, landscape and personality of the state. These facts, superlatives and rankings are just a start to unearthing the unique perspectives rewarded from a trip to The Land of Enchantment.
Press Room Home | Anniversaries | Story Ideas | What's New | Rankings & Superlatives
REGION FAST FACTS
Three of the U.S.'s 23 United Nations Educational, Scientific and Cultural Organization's (UNESCO) World Heritage sites are in New Mexico – Carlsbad Caverns National Park, Chaco Culture and Taos Pueblo. New Mexico is the only state with more than two sites.
New Mexico ranks as the nation's fifth largest state and sixth nationally in total acreage of public lands.
New Mexico's 14 national monuments are the third-most among all states in the U.S.
New Mexico is second only to Arizona in days of sunshine per yet.
One-fourth of the state is forested with seven National Forests across the state including the nation's largest, the 3.3 million acre Gila National Forest, which includes the Gila Wilderness.
Route 66 spans over 600 miles of the state and is listed as one of New Mexico's Scenic and Historic Byways.
The New Mexico state question is "Red or Green?" which refers to the option of red or green chile when ordering New Mexican food. Chile is featured in every meal from breakfast through dinner and is the No. 1 cash crop in the state as New Mexico grows more chiles than any other state with over 120,000 tons produced annually.
The state's elevations range from 2,871 feet at Red Bluff Lake to 13,161 on Wheeler Peak.
New Mexico has six of the seven life zones found in the world – classified by vegetarian type and varying by altitude and orientation to the sun.
New Mexico is known as "The State of the Arts" because it is home to more working artists, open studios, artist-owned galleries and specialty and artisan-oriented shops than any other state per capita.
The state has five national forests, 20 national parks and monuments, 35 state parks (20 of which have lakes), 25 scenic byways and 21 wilderness areas.
The Rio Grande, which ribbons through the state from north to south, is the fourth-longest river in the country.
The state receives 200-300 inches a year of snow.
Nineteen pueblos and three reservations span across the state with 22 tribes represented.
Smokey Bear originated in New Mexico as an actual black bear cub found by firefighters from Taos Pueblo in southeaster New Mexico during the massive Capitan Gap fire of 1950.
The Wilderness Act was signed into law in September 1964 by President Lyndon B. Johnson, with the Gila Wilderness the first official designated Wilderness Area in the nation.
New Mexico is the only state with night light legislation enacting the Night Sky Protection Act in 1999.
Home to the two highest elevation zipline tours in the U.S. (Apache and Angel Fire).
New Mexico is one-fourth of the Four Corners Monument, which is the only unique landmark in the U.S. where four states intersect at one point.
Chaco Culture National Historic Park dates back to 850 AD and is considered the most exceptional concentration of pueblos in the American Southwest.
Gallup is the Indian Jewelry Capital of the World.
The Palace of Governors in Santa Fe is the oldest continuously occupied public building in the United States.
Santa Fe is the nation's highest elevation capital (7,000 feet) and nation's oldest capital.
San Miguel Chapel is the oldest standing church in the U.S. (1610).
The Taos Pueblo is one of the oldest continuously occupied communities in the U.S. with the 900-year-old buildings still occupied.
Taos Pueblo is the only active Native American community designated as both a UNESCO World Heritage Site and a National Historical Landmark.
Red River has the longest summer tubing tracks in the country.
Santa Fe has been a UNESCO Creative City since 2005 – the first city honored as such.
The world-famous Santa Fe Opera has an open-air theater situated dramatically outside of the capital in the foothills of the Sangre de Christo Mountains.
Las Vegas has 900 buildings in nine historic districts on the National Registry – more than any city in the United States.
More than 500 dinosaur tracks were discovered at Clayton Lake State Park in the early 1980s. Their age is estimated at 100 million years, in the late age of dinosaurs.
The Capulin Volcano (8,000 feet) is so tall that five states can be seen from the top: New Mexico, Colorado, Kansas, Oklahoma and Texas.
White Sands National Monument contains the largest gypsum dune field in the world. The result of water evaporating from transitory lakes with a high mineral content, the gypsum deposits span 275 square miles.
With Albuquerque considered the "Hot Air Ballooning Capital of the World," the Albuquerque International Balloon Fiesta has been touted as the most photographed event globally.
The American International Rattlesnake Museum in Albuquerque is home to the largest collection of live rattlesnakes in the world.
The Gila National Forest is the largest in the country.
Las Cruces holds the Guinness World Record for making the largest enchilada (done every year at the city's Whole Enchilada Festival) using 75 gallons of red chili sauce and 750 pounds of ground corn to make the tortillas.
Southern New Mexico is the oldest wine making region in the country producing grapes for over 400 years since the arrival of Spanish colonists and their "mission" grapes.
Hatch is known as the "Green Chile Capital of the World"
The city of Truth or Consequences was once called "Hot Springs" before the town changed its name to the title of a popular radio quiz program in 1950.
REGION SUPERLATIVES & RANKINGS
TravelSquire's "Top 28 Destinations for 2017"
Travel + Leisure: New Mexico ranks as "The Next Big Ecotourism Destination"
The Active Times/The Huffington Post: New Mexico ranks fourth as "Unforgettable Bucket List Trips You Can Do on a Budget" (May 2016)
Lonely Planet: Southern New Mexico one of "Best in the U.S. Top Ten"
CNN.com: Green Chile Cheeseburger Trail one of "16 Intriguing things to see and do in the U.S. in 2016"
The Active Times/The Huffington Post - Unforgettable Bucket List Trips You Can Do on a Budget (May 2016)
Los Angeles Times: Chaco Culture National Historic Park named one of 14 "Hottest Desert Park Destinations in the Southwest"
USA Today and 10Best Reader's Choice Travel Awards: Chaco Culture National Historic Park (No. 10) and Bandelier National Monument (No. 8) among "Top 10 Archaeological Sites in America"
Map and atlas publisher Rand McNally named Gallup "America's Most Patriotic Small Town"
USAToday and 10Best Reader's Choice Awards: Ship Rock is "America's No. 1 Best Geological Formation"
Town and Country Magazine: Las Vegas named "One of the Best Small Towns for Antique Shopping"
Departures: Casa Grande at Vermejo Park Ranch, New Mexico named "Best of 2016: The 15 Most Important New Luxury Hotels"
Travel + Leisure: Santa Fe ranked as No. 2 "The Best Cities in the U.S." (July 2017)
Travel + Leisure: Santa Fe ranked as No. 11 "The Best Cities in the World" (July 2017)
Ojo Caliente and Sunrise Springs ranked as No. 3 and No. 4, respectively, "Top 10 Domestic Destination Spas" (July 2017)
National Geographic World Legacy Award Winner - Sense of Place: Santa Fe (March 2017)
Jetsetter: Taos, Nine Cutest Small Towns in America (May 2017)
National Geographic: Santa Fe, America's 20 Best Mountain Biking Towns
TripAdvisor: Most Charming Ski Vacations in America: Santa Fe and Taos
Thrillist: Santa Fe, The (R)evelation of Steve Jobs and Meow Wolf's "The House of the Eternal Return" listed as "15 Coolest Things Coming to U.S. Cities in 2017"
Outside Magazine: Taos/Southwest Slopes listed as "The Best 16 Ski Destinations in the U.S." (December 2016)
TheRevver: Santa Fe named one of 26 "Best Motorcycle Locations in the U.S."
Dream Vacation Magazine: Angel Fire Resort ranked a Top 25 "Best Family Ski Resort in North America"
Realtor.com: Angel Fire Resort ranked ninth as one of "America's Most Affordable Ski Towns"
Travel + Leisure: Red River ranked No. 18 on "America's Favorite Towns"
SpaFinder Wellness Awards: Ojo Caliente named one of the "Best Hot Springs" (November 2016)
SpaFinder Wellness Awards: Sunrise Springs Spa & Resort named one a "Best Ahead of the Curve Innovation" (November 2016)
American Spa Magazine: Sunrise Springs Spa & Resort named "One of the Best New Spas in the U.S." (December 2016)
AARP: Santa Fe named No. 1 "U.S. Cities for Art Lovers"
Gear Patrol: Santa Fe named to "The Adventure List: 25 Best Places to Travel in 2016" (May 2016)
Money Magazine: Santa Fe ranked No. 7 "Best Domestic Destination"
Livability: Santa Fe ranked No. 2 as "The 10 Best Places for Wine"
Open Table: Santa Fe "Top 25 Most Romantic Cities in America"
Travel + Leisure: Enchanted Circle Scenic Byway among "America's Best Fall Foliage Drives"
Conde Nast Traveler: Santa Fe No. 5 "Best Shopping Cities in America" (September 2016)
Conde Nast Traveler: Santa Fe No. 3 "Best Small Cities in the U.S." (October 2016)
Conde Nast Traveler: Santa Fe No. 8 "Friendliest Cities in the World" (August 2016)
Thrillist.com: Santa Fe is one of "10 American Cities with the Coolest Architecture"
Conde Nast Traveler: Santa Fe No. 2 in "Best Places to Retire in the U.S."
USA Today and 10Best Reader's Choice Travel Awards: Taos ranked as top choice for "Best Ski Town" (November 2016)
National Geographic: Taos Among "World's Best Ski Towns"
TripAdvisor: Taos named one of "24 Top Beautiful Little Mountain Towns Across America" (September 2016)
U.S. News Travel: Sunrise Springs Resort in Santa Fe is one of "10 Top Wellness Retreats Around the Globe"
Women's Health & Yelp: Albuquerque among The 15 'Wellthiest' Cities in the U.S. (July 2017)
Budget Travel: Albuquerque named one of "25 Most Beautiful Cities in America" (March 2017)
Thrillist: Albuquerque among "The Best American Cities for Creatives" (July 2016)
Outdoor Magazine: Albuquerque named one of the "10 Best Big Cities for Active Families" (2016)
Redbook: Albuquerque named "No. 1 Getaway for Solo Female Travelers" (November 2016)
Bicycling.com: A ride along the Rio Grande in Albuquerque is one of "The Most Romantic Rides in the US"
Vagabondish.com: Albuquerque is "America's Coolest City for Geek Travelers"
USAToday: Albuquerque's Southwest Chocolate & Coffee Fest a top choice in "America's Sweetest Annual Festivals"
Travel + Leisure: Albuquerque is No. 1 in "America's Best Cities for Picnics"
Travel + Leisure: Albuquerque one of "America's Best Beer Cities" (No. 17)
Travel + Leisure: Albuquerque one of "America's Best Cities for Festivals" (No. 8)
Travel + Leisure: Albuquerque one of "America's Best Coffee Cities" (No. 4)
Travel + Leisure: Albuquerque one of "America's 20 Most Charming Cities"
Budget Travel: Albuquerque one of "America's Most Beautiful Cities"
USAToday: Jemez Springs one of "10 Small Towns That Go All Out for Christmas"
USAToday: Sierra Grande Lodge & Spa in Truth or Consequences one of "9 Hot Tubs for Soaking up the Scenery"
Recollections.biz: New Mexico Farm & Ranch Heritage Museum one of the country's "Top 30 Historical Preservation Societies"
USAToday: Pie Town Pie Festival a top choice in "America's Sweetest Annual Festivals"
Realtor.com: Ruidoso ranked fifth as one of "America's Most Affordable Ski Towns"
Lonely Planet: New Mexico Museum of Space History one of "50 Museums to Blow Your Mind"


William started school today in the 2year old classroom. He had no tears as I left. It will either hit him in 2hours or he will be a champ.
Meanwhile, I am here feeling as empty as can be as my two kiddos are in school. Maybe the liberated feeling will come later?


Colorfully designed map of Bohol Island which illustrates main attractions such as: Philippine Tarsier, Chocolate Hills, Can-umantad Falls, Dimiao Twin Falls, River Cruise, Watch tower, Alona Beach on Panglao Island and also Pamilacan and Balicasag Islands.
Keep your memory from travelling to the beautiful Bohol Island by this original map card. Bohol Illustrated Map postcard is a creative Philippine souvenir which can be used in official postcrossing, snail mail and postcard swap.


Looking for WOW factor? Host your next event at the Boch Center located in the heart of Boston. We have endless possibilities and can accommodate a corporate awards ceremony, product launch, graduation, gala or wedding in our opulent spaces at both the historic Wang and Shubert Theatres.
Learn more about our event spaces below.
Contact Function Sales at booking@bochcenter.org for more information.
Give your event a unique flair in this historic, lavish, 3,600-seat theatre. As one of the largest stages in the country, the Wang Theatre offers a remarkable opportunity to hold an elegant dinner or fundraising gala right on stage. The spacious Wang Theatre can hold an event up to 2,000 using all event spaces.
The Grand Lobby's one hundred foot domed ceiling, grand staircase, fifteen-foot chandeliers, and opulent décor create an event backdrop of unmatched elegance. The Loge Promenade and Third Floor Lobby offer welcoming balconies overlooking the spectacular Grand Lobby. Modeled after the Palace of Versailles, all Grand Lobby locations will make your guests feel like royalty.
Below the famous Grand Lobby lies a hidden gem, the Lower Lobby. This elegant ballroom style room boasts warm walnut wood walls inlaid with gold, the perfect touch of class for your event.
The 1500-seat Shubert Theatre, or 'The Little Princess' of Boston's Theater District, is a sophisticated setting for a corporate meeting, and the stage is intimate enough for all types of celebrations.


Exciting growth potential and exciting projects at one of Chicago's top GCs!
One of Chicago's up and coming public and private general contractors in the southwestern suburbs is looking for a strong PM.
You will work with the current chief estimator to calculate costs from proposals, blueprints, specifications, and related estimating documents for multiple edgy projects throughout Chicago. Also, you will prepare budgets for all phases of the project. You will be working on highly visible construction projects and making an immediate impact with the opportunity to take over the Chief Estimator position quickly.


Entropy is the opposite of Information.
Played a total of 8 levels.
"I really like dogfighting without vulcan or fusion. Well played."
"great game! tough to do anything without vulcans. ;-)"
"This level is great, you flew really well. Always had homers to shoot lol"
"Great game, well flown jedi"


Recognising the critical importance of sound valuations to risk management, financial reporting and regulatory capital adequacy, in early 2007 the Basel Committee on Banking Supervision initiated a project to gain a deeper understanding of approaches used to determine valuations of complex financial instruments. Within the context of existing accounting standards, the Committee's work focused on the use of valuation methodologies for risk management and financial reporting purposes. It also assessed the related control, audit and governance practices surrounding fair value measurement. In response to the market turmoil that emerged during mid-2007, the scope of the work was subsequently expanded to include coverage of how banks responded to the market stress and initial lessons learned.
Drawing on the work of the Committee's Accounting Task Force and Risk Management and Modelling Group, this paper summarises the Committee's initial assessment of valuation practices. To strengthen practices and promote greater transparency regarding valuation processes, the Committee is undertaking further work to develop guidance that supervisors can use to assess the rigour of banks' valuation processes and promote improvements in risk management and control practices. The Committee will also work with accounting and auditing standard setters and auditors to promote standards and practices that enhance the reliability, verifiability and transparency of fair value estimates. These initiatives will build off existing Basel Committee and industry guidance. They are also part of a broader effort by the Committee and national supervisors to strengthen firm-wide risk management practices.


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Review: How (Not) To Live in Suburbia at The Soho Theatre
by Greg Stewart
Annie Siddons How Not To Live in Suburbia
Annie Siddons' How (Not) to Live in Suburbia has returned to the Soho Theatre following a sell-out run earlier this year. This autobiographical play melds traditional storytelling with theatre and multi-media, the overall effect is an accomplished and compelling piece of theatre.
At times, it resembles a TED Talk, with Annie speaking directly to the audience, sharing her story in impressively complex alliteration and prose, which is almost hypnotic to listen to. Where a PowerPoint presentation would be, Annie's monologues are intercut with video montages and music, which provide further context to the story she is telling.
A Londoner through and through, Annie moves to Twickenham – Home of Rugby, but struggles to fit in to the zone 5 crowd. Her attempts at social interaction never come to fruition and she suddenly finds herself desperately lonely. Metaphors are used heavily throughout and she describes her loneliness as a Walrus who is always with her; smelly and unpleasant it prevents her from really achieving her goals, despite frequently penning her own 'manifesto's'.
Annie herself is very open and natural, you immediately warm to her as a storyteller and find yourself deeply fascinated in the story she has to tell. It's clear she's not afraid to tell things like it is, making the performance feel extremely unmasked and laced with an acute wit.
Soho Theatre Announces Release Date for World Premiere of Typical on Soho Theatre On Demand
New Soho Theatre On Demand Films Comedy Masterclasses and Pilot Comedy Shows Announced
Annie is joined on stage periodically by Nicki Hobday, who co-directs How (Not) to Live in Suburbia along with Justin Audibert. Hobday plays Annie in caricature form, emphasising the decisions she's made and the hurt that she feels. It's thought provoking to watch the real Annie take a step back and look at 'herself' in a different place and time. Nicki Hobday is great in the role, and in one particular scene, where she starts sending drunken texts, it's a gloriously cringeworthy and hilarious at the same time.
As well as pausing for Hobday to take the reins, Siddons also has time to reflect during the short films, directed by Richard DeDomenici. These feel modern and of the social era, some like something you'd see on Snapchat, others more carefully devised mini-feature-films, allowing a wider circle of characters to be introduced. The more traditional theatre-goer may have reservations about such prolific use of video, but it adds so much depth, and indeed comedy, to the overall performance.
How (Not) to Live in Suburbia achieves something most TED Talks can only dream of, it allows Annie Siddons to tell a very personal and emotive story, in a refreshing and thoroughly enlightening fashion, more engrossing than even the most attractive PowerPoint can achieve. The metaphorical nature of the production allows the audience to relate to the circumstances; to understand but not judge. This is a fantastic example of modern theatre and shows that even a smaller scale production can still have a huge impact on those who see it.
How (Not) To Live in Suburbia at The Soho Theatre
Greg is an award-winning writer with a huge passion for theatre. He has appeared on stage, as well as having directed several plays in his native Scotland. Greg is the founder and editor of Theatre Weekly
Soho Theatre Signs Three-Series Deal With Amazon Prime Video in the UK
Amanda Wilkin Wins Varity Bargate Award 2020 with Shedding A Skin
Review: Philip Pullman's Grimm Tales from Unicorn Theatre (Online)
Verity Bargate Award 2020 Shortlist Revealed

redpajama_set_name: RedPajamaCommonCrawl


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West Texas briefs, 1/10/16
News in brief from around the Concho Valley
West Texas briefs, 1/10/16 News in brief from around the Concho Valley Check out this story on gosanangelo.com: http://www.gosanangelo.com/story/news/local/2016/01/09/west-texas-briefs-11016/92011550/
Standard-Times Published 2:04 p.m. CT Jan. 9, 2016
Fort McKavett
1800s cuisine focus of cooking event
The Fort McKavett State Historic Site will present "Warm Hearths Make Full Bellies: Cooking in the 19th Century" from 10 a.m. to 4 p.m. Saturday at 7066 Farm-to-Market Road 864.
Visitors will discover how people in the 1800s made the culinary creations that fed the troops, as well as officers and families, experiencing 19th century cooking and enjoying home-cooked food as it would have been prepared.
Standard admission fees apply. For more information, call 325-396-2358.
Learning institute offers senior classes
Registration is open for three classes from the Osher Lifelong Learning Institute at the Texas Tech Center, 254 Red Raider Lane.
"Rainwater Harvesting for Birds and Wildlife" will be 9 a.m. to noon March 3.
"Birds of Central Texas: Behavior, Identification and Observation" will be 9 a.m. to noon March 10.
"Do We Have the $ and Sense, Environmental Literacy and Ethics to Protect Our Natural Resource Capital? Probably Not!" will be 2:30-4 p.m. April 7.
OLLI is a unique learning community for adults 50 and older. To register for classes or more information, call 830-990-4948 or visit www.depts.ttu.edu/outreach/hillcountry.
Rural Shakespeare documentary shown
The Odeon Theater will present the documentary film "Bard in the Back Country" at 6:30 p.m. Wednesday.
The film takes a look at the business of performing Shakespeare's work in rural America. It chronicles a summer with a professional theater company based in Bozeman, Montana. Viewers get a behind-the-scenes look at the 10 actors on tour as they cover more than 6,500 miles to perform Shakespeare in the rural towns of the region.
Admission is free. For more information, call 325-347-7758 or email c.conlee@yahoo.com.
Mason/Junction
Classical musicians to give free concerts
The Mason County M. Beven Eckert Memorial Library will sponsor a free concert by the Piatigorsky Foundation at 6:30 p.m. Thursday at the First United Methodist Church, 210 Broad St.
Violist Marcus Thompson and pianist Doris Stevenson will perform. Thompson debuted at Carnegie Hall in 1968 and has since performed for audiences all over the world as a concert soloist, recitalist and in chamber music. Stevenson has performed with the Boston Pops, at Carnegie Hall, the Kennedy Center and many other venues. She has been described as an "artist of the highest order."
A free concert also will be held in Junction at 6:30 p.m. Friday at First Baptist Church, 208 S. Eighth St. A reception with the artists will follow. The concert is sponsored by the Junction Texas Tourism Board and the Kimble County Library Board. For more information, call 325-475-2271.
Concerts also will be held at 6 p.m. Monday in the Scurry County Museum in Snyder and at 7 p.m. Tuesday at the Fort Stockton Public Library.
Read or Share this story: http://www.gosanangelo.com/story/news/local/2016/01/09/west-texas-briefs-11016/92011550/
Dog in July 3rd video surrendered by owner
Abilene police catch San Angelo homicide suspect
Rescue seeks help after dogs dumped near Sonora
Police ID man killed at Red Roof Inn
Man who struck officer sentenced to probation
Girl injured in boating accident at Nasworthy


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> Maritime>The Cod Hunters
The Cod Hunters
The incredible story of the Shetlanders who...
The incredible story of the Shetlanders who fished for cod around Faroe, Rockall, Iceland and Greenland.
By John Goodlad.
For many years John Goodlad was the voice of the Shetland fishing industry when, as CEO of the Shetland Fishermens Association, he represented the interests of Shetland fishermen in Edinburgh, London, Brussels and beyond. He then became a fish farmer, specialising in the production of organic salmon. John now spends a lot of his time in London working for a large seafood Investment Fund. He is also a Director of several companies and advises a number of national and international fisheries organisations. John has always been interested in the history of the Shetland fishing industry and his story of the Faroe smacks is a personal and very readable account of the incredible story of the Shetlanders who fished for cod around Faroe, Rockall, Iceland and Greenland. This is no dry history of boats and fish - it is a story about ordinary people who did extraordinary things.
This is however much more than a book about Shetland and Shetlanders. The geographical scope is extensive; from the remote fjords of west Greenland to the elegant dining tables of nineteenth century London, from the fishing banks off Faroe to the best restaurants in the Basque country, from the taverns of Tarshavn to the prison cells of Lerwick. The stories of several very interesting people are told. What do a retired railway worker in New York, an embittered Fishery Officer in Lerwik, a bankrupt banker, the daughter of the famous explorer David Livingston and a Faroese Prime Minister have in common? They were all involved in some way in the story of the Shetland cod hunters.
Sailing with Steam
Billy Cardno's recollections of what it was like to serve on the Orkney...
Orkney's Lifeboat Heritage
The story of Orkney's volunteer lifeboat crews. By Nicholas Leach. Paperback.
Stromness - A History
Bryce Wilson traces the town's colourful history over 400 years, with tales...
The British Sailor of the Second World War
This book tells the dramatic story of how the Royal Navy transformed ordinary...
Wooden Fishing Boats of Scotland
James A. Pottinger's new illustrated volume concentrates solely on the...
Scottish Fishing Boats: A New Look
A companion to his successful first book, Fishing Boats of Scotland, James...
Launch the Margaret Foster!
Kirkwall Lifeboat Reports 2001 - 2013 Compiled by Graham Campbell
HMS Hampshire: a Century of Myths and Mysteries Unravelled
Twelve authors with local knowledge pool their expertise to sort fact from...
Scottish Lighthouse Pioneers
Travels with the Stevensons in Orkney and Shetland. By Paul A. Lynn. Paperback.
Jutland - The Unfinished Battle
'A compelling dramatic account of the Royal Navy's last great sea battle' By...
Memories of the S.S Earl Thorfinn
Billy Cardno served as a cabin boy on the S.S Earl Thorfinn from 1958 until...
Mutiny On The Spanish Main
HMS Hermione and the Royal Navy's revenge By Angus Konstam. Hardback.
Bombay to Elwick Bay
Bill and Sylvia Dennison's stories of their travels and life aboard their...
The British Lighthouse Trail
A Regional Guide. The ultimate guide to all major and minor lighthouses in...
Frozen in Time
The Fate of The Franklin Expedition. A fully updated edition of the amazing...
Erebus: The Story of A Ship
Michael Palin explores the world of HMS Erebus, whose last British port of...
Northlink Ferries: The Islands' Lifeline
Covering the history of ferry services to Orkney and Shetland, this new and...
Saint-Faust In The North 1803-1804
'Orkney and Shetland in danger'. The adventure of Saint-Faust, who...
The Guns And Armour of Scapa Flow
A Modern Photographic Quest to the Shipwrecks of an Imperial Naval Saga. In...
After The Lost Franklin Expedition - Lady Franklin and John Rae
Peter Baxter takes a new approach to the history of the Franklin Expedition,...
Icebound in the Arctic
The Mystery of Captain Francis Crozier and the Franklin Expedition. A Great...
No Earthly Pole
The Search for The Truth About The Franklin Expedition 1845. By Ernest C....
From Scapa To Jutland
The story of the HMS Caroline at war from 1914-1917. By John Allison. Paperback.
The Pirate World
A History of the Most Notorious Sea Robbers. By Angus Konstam. Hardback.
John Rae's Arctic Correspondence 1844-1855
This collection of personal correspondence - reissued here for the first time...
Sir John Franklin's Erebus and Terror Expedition: Lost and Found
1845, Sir John Franklin set out to find the North-West Passage. His ships...
For The Safety of All
A Story of Scotland's Lighthouses. By Donald S Murray. Hardback.
Orkney Tide Tables 2022
Orkney Tide Tables 2022. Pocket-size booklet.
The Orkney Yole
This book is a celebration of the Orkney Yole. It has been compiled by...
Orkney Media Group Ltd. SC315893 , The Orcadian Bookshop 50 Albert Street Kirkwall KW15 1HQ
Email: bookshop@orcadian.co.uk


Does anyone else have a problem accessing their inbox from the app? Everything else works perfectly fine except I can't open my inbox tab. I didn't have this issue until the last 2 weeks or so. I keep having to log onto an internet browser any time I have a private message.


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Deep Reinforcement Learning for Quantum State Preparation with Weak Nonlinear Measurements
Riccardo Porotti Max Planck Institute for the Science of Light, Erlangen, Germany Department of Physics, Friedrich-Alexander Universität Erlangen-Nürnberg, Germany Antoine Essig Univ Lyon, ENS de Lyon, CNRS, Laboratoire de Physique,F-69342 Lyon, France Benjamin Huard Univ Lyon, ENS de Lyon, CNRS, Laboratoire de Physique,F-69342 Lyon, France Florian Marquardt Max Planck Institute for the Science of Light, Erlangen, Germany Department of Physics, Friedrich-Alexander Universität Erlangen-Nürnberg, Germany
Quantum control has been of increasing interest in recent years, e.g. for tasks like state initialization and stabilization. Feedback-based strategies are particularly powerful, but also hard to find, due to the exponentially increased search space. Deep reinforcement learning holds great promise in this regard. It may provide new answers to difficult questions, such as whether nonlinear measurements can compensate for linear, constrained control. Here we show that reinforcement learning can successfully discover such feedback strategies, without prior knowledge. We illustrate this for state preparation in a cavity subject to quantum-non-demolition detection of photon number, with a simple linear drive as control. Fock states can be produced and stabilized at very high fidelity. It is even possible to reach superposition states, provided the measurement rates for different Fock states can be controlled as well.
Modern quantum technologies in their various incarnations, ranging from sensing to computation, rely on quantum control as an essential part of their toolbox. The numerical optimization of pulse sequences, using powerful methods such as GRAPE [1] , has by now become an essential aspect of many experiments. Although important challenges remain in this domain, e.g. in multi-qubit control, there is another frontier where the optimization of control strategies is comparatively much less developed: the regime of feedback-based control [2, 3, 4, 5, 6, 7, 8, 9, 10] . The automatic discovery of feedback strategies, which include real-time decision making based on previously observed measurement results, is challenging for two reasons: First, standard optimal control techniques are not applicable, and second, feedback leads to a drastic expansion of the search space, involving a doubly-exponential growth of the number of strategies vs. the number of time steps. Deep reinforcement learning (RL) [11] is a general approach that offers a possible solution for this challenge, with powerful recent examples in other areas, ranging from computer games [12] to robotics [13] . Deep RL for discovering quantum feedback strategies was first introduced in [14] where it was illustrated in the application of quantum error correction. The idea behind deep RL is that a neural-network based "agent" makes observations and suggests corresponding actions. During the learning phase, the agent discovers from scratch novel strategies, in a systematic procedure which at first resembles trial and error but later begins to build on insights acquired earlier. That RL in general and deep RL in particular is a powerful approach in quantum physics has by now been demonstrated in a variety of tasks in different areas: in most works so far, these tasks did not yet require real-time feedback involving decision-making based on physical measurements, but RL already proved itself to be a versatile tool even in those settings [15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26] . These publications are part of a larger drive towards the use of machine learning tools for quantum experiments (e.g. [27, 28] ). A small number of works have explored the use of various RL(-related) techniques to optimize feedback or adaptive measurement protocols [29, 30, 31] . Investigations of deep RL, which promises to be a powerful approach in the domain of measurement-based quantum feedback, are still rare. Beyond [14] , there have been some very interesting additional works in this direction. These include optomechanical feedback cooling [32] as well as state preparation and stabilization in a potential with position measurements [33, 34] . Very recently, feedback-based quantum state preparation in a cavity-qubit system [35] was proposed, for a setting employing continuously parameterized quantum circuits.
Figure 1: Reinforcement learning for discovering feedback control based on continuous measurements. a) A neural-network-based agent can find a strategy to control a quantum system, compensating for simple (linear) controls by extracting information from advanced (nonlinear) measurements. b) Schematics of the model considered in the main text (see [36] ): a qubit is illuminated with multiple frequencies in order to probe the number of photons n in a coupled cavity. The information can be extracted by observing the reflected radiation in each of the channels. The reduction in amplitude in the outgoing wave indicates detection of the Fock state corresponding to the respective channel. c) Timetraces of the quadrature signal observed in the homodyne measurement channels of Eq. 5. In this particular example, the cavity state decays at an intermediate time.
Deep RL for measurement-based quantum feedback can be used to address experimentally relevant questions where no general straightforward answer is available. While general results exist in the absence of feedback, e.g. which terms in a Hamiltonian need to be controllable to produce arbitrary unitaries, such insights are not available for feedback-based strategies. One obviously important example concerns the question whether one can compensate for simple control (e.g. purely linear operations) by exploiting more advanced nonlinear measurements (Fig. 1a). In the present work, we answer this question by investigating an example inspired by recent experimental progress: a harmonic oscillator where individual Fock states can be continuously monitored in a quantum non-demolition fashion. The corresponding experimental setting (demonstrated in [36] ) concerns a microwave cavity where the precise Fock state number can be monitored by driving a coupled qubit with a frequency comb.
Using a state-of-the-art deep RL approach, we show that under these conditions a mere displacement drive, conditioned on the measurement outcome, is sufficient to produce and stabilize Fock states. Even superpositions of such states can be stabilized to a good degree of fidelity, provided the control is expanded to include adaptive measurement rates.
Ii Physical system
Measurement-based feedback for quantum physics, using digital controllers, dates back to 2011 with a pioneering experiment that demonstrated the stabilization of a Fock state in a microwave cavity using the information extracted by interacting flying circular Rydberg atoms [37] . Each circular Rydberg atom encodes one bit of information about the number of photons in the cavity. From the extracted information, a classical controller computes the density matrix of the cavity in real time. It can then react in one of two ways. Either it sends a resonant atom that can add or subtract a photon to the cavity [38, 39] , or it sends a classical microwave drive whose amplitude is chosen to displace the cavity state in an optimal way [37] . The latter case makes a fascinating use of quantum measurement backaction as it is the combined effect of a classical drive and quantum measurement that steers the cavity towards the targeted Fock state.
Several experiments with Rydberg atoms or superconducting circuits have by now been realized to count the number of photons in a microwave resonator [40, 41, 42, 43] , enabling them in principle to implement quantum feedback strategies of this sort.
The present work is heavily inspired by a recent implementation [36] demonstrated a way to acquire information about the number of photons in a resonator in a continuous manner. It exploits a qubit, coupled to both the cavity and a measurement transmission line, driven by multiple frequencies simultaneously, each of which is sensitive (via dispersive qubit-cavity coupling) to one Fock state. To each number of photons n in the storage mode is associated one outgoing mode of the transmission line at frequency fn. If we change the reference frame by displacing the outgoing modes by the opposite of the input coherent state, we can say that a single mode is excited and all the non-resonant modes are in the vacuum state. Therefore, Fock states |n⟩ correspond to all outgoing modes in the vacuum state, except the one at frequency fn.
We model this physical system as shown in Fig. 1b. That figure represents a cavity, subject to a continuous weak measurement which reveals the Fock state number at any given moment in time. This works by reading out multiple measurement channels in parallel, where the signal in channel n will assume a different average value whenever the cavity is in Fock state |n⟩, as described before. In presence of a pure Fock state, without noise, only one signal would be excited to 1 and the others to 0, resulting for example in (0,1,0…,0) for state |1⟩. This measurement introduces an unavoidable intrinsic fluctuating back-action but also tries to localize the system into some random Fock state according to the Born rule - not necessarily the desired target state. Any kind of control must both counteract and exploit these tendencies introduced by the measurement [38] .
Iii Model
Apart from drive and decay (which we will introduce later), the Hamiltonian of the cavity subject to the Fock state QND measurement can be expressed as
^Hmeas=∑nχn^an†^an^Pn, (1)
where ^Pn=|n⟩⟨n| is the projector on Fock state n of the cavity and we have switched to a frame rotating at the cavity resonance frequency. We will choose χn=1 for all n from now on. The mode ^an refers to the n-th measurement channel, which will be monitored to assess the cavity state, and which can be treated like a driven, decaying bosonic mode. At this stage of the effective description, we have already eliminated the qubit which is coupled to the cavity and which provides the nonlinearity needed for the Fock state measurement. When a homodyne measurement is performed on the phase quadrature of any of the measurement channels, the resulting evolution of the cavity's reduced density matrix, obtained after integrating out the measurement channels ^an, can be described by a stochastic master equation (SME) [44, 4] . With ^A=^Pn as the measurement operator, the contribution to the SME from this channel reads:
d^ρ=γmeasD[^A]^ρ(t)+√γmeasH[^A]^ρ(t)dW(t) (2)
where γmeas is the measurement rate of a perfectly efficient measurement, D[^A] is the measurement-induced Lindblad superoperator,
D[^A]^ρ=^A^ρ^A†−12(^ρ^A†^A+^A†^A^ρ), (3)
and H[^A] is
with ⟨^A⟩=Tr[^A^ρ].
The stochastic part of Eq. 2 contains dW(t), which is an infinitesimal Wiener process, with zero mean and ⟨dW(t)2⟩=dt. The Wiener process is experimentally obtained from the homodyne measurement record, which reads
√γmeas/2Tr(^Aρ+ρ^A†)dt+dW(t). (5)
Combining the effect of all the simultaneous measurements, plus an additional coherent drive, we obtain the Itô stochastic differential equation of motion for the quantum state of the cavity:
d^ρ=−i[^H(t),^ρ(t)]dt++∑nγn2D[^Pn]^ρdt++√γn2(^Pn^ρ+^ρ^Pn−⟨2^Pn⟩^ρ)dWn(t) (6)
where, if not otherwise mentioned, we choose the same rate γn=γmeas for all channels and the Wiener processes dWn(t) associated to every channel are uncorrelated ⟨dWn(t)dWm(t′)⟩=δ(t−t′)δnmdt. Here ^H(t) describes a time-dependent drive that is used to control the cavity conditioned on the measurement outcomes, implementing feedback control. That is,
H(t)=i(β(t)^a†−β∗(t)^a) (7)
where β(t) is the complex time-dependent drive amplitude.
Figure 2: Reinforcement Learning pipeline. A fully connected neural network receives the quantum state of the cavity (represented via the density matrix) at timestep t. The network outputs the suggested action (i.e. real and imaginary part of the displacement drive) to be applied to the cavity in that particular timestep. The driven dissipative quantum system is evolved following Eq. 2 in order to obtain the density matrix at the next timestep t+1. Furthermore, the instantaneous reward value r(t) is computed from the current state fidelity and enters the loss function L. We are using an actor-critic method, in which the current quantum state is also assigned a value function V(t), by another neural network, which helps in searching for the optimum feedback strategy.
Figure 3: Reinforcement learning for cavity state preparation: Fock state results. a) Training progress of the RL agent (here, for reaching Fock state |3⟩). Upper panel: Cumulative reward (i.e. the sum of all instantaneous rewards during a trajectory) during different training episodes (the solid line is the average). The return is normalized in the sense that having the maximum reward in each timestep would result in R=1. Lower panel: Final state fidelity in each episode (maximum 1.0). b) Example of a trajectory after the training is completed. Upper panel: Fock state probability distribution of the cavity, where the stochasticity stems from the weak Fock state measurement. Wigner densities corresponding to times [0,1/3,2/3,1]Tmax are shown. Lower panel: Real (blue) and imaginary (red) part of the displacement drive. The actions are capped in range to ±βmax. c) Same information, but averaged over 50 trajectories. d) Results after training, in terms of final infidelity, averaged over multiple trajectories, displayed for different networks trained on reaching different final target Fock states. Lower infidelities (compared to the training shown in panel a) are reached by turning the policy into a deterministic one, always taking the action corresponding to the peak of the Gaussian distribution suggested by the neural network. Note logarithmic axis. Saturated color represents the RL approach, pale color the greedy strategy (performing much worse). In all examples, γmeas=20 and βmax=20.
The main point that we want to stress is that the control is completely linear, while the nonlinear part of Eq. 2 is given by the homodyne measurement only.
The goal that we want to achieve with the help of a suitable feedback-based control strategy is quantum state preparation, i.e. to drive an arbitrary initial state to a prescribed target state.
Iv Reinforcement Learning
In this section we will show how Reinforcement Learning (RL) can be applied to the problem at hand. In general, Reinforcement Learning tries to discover good feedback-based control strategies. An "agent" observes the world (typically called "environment") and takes an action based on that observation ("state"). The mapping from observation to action defines the strategy ("policy"). Modern powerful RL variants, such as the one we will be using here, produce this mapping with the help of a deep neural network. The aim of RL training is to maximize a reward that has been designed to express the goal of the particular task at hand. This is done via running many thousand trajectories, improving the policy gradually, e.g. via some version of gradient descent on parameters that determine the policy (i.e. typically the parameters defining the agent's network). In our case, we want to control the cavity (the RL "environment") via a linear drive, based on the noisy measurement trajectories of the nonlinear measurement. The goal is to prepare some desired quantum state and stabilize it against noise, e.g. noise coming from the measurement itself or from external decay or decoherence. The RL environment is modeled using a simulation of the physical time evolution of the cavity, employing the SME introduced above, which we solve with a Runge-Kutta method with additive noise. The physical time t is discretized in Nmax timesteps, such that t can only assume discretized values tj=Δt⋅j with j∈[0,Nmax]. At each time tj, the environment receives an action aj, carries out this action, and outputs the next observation sj+1 and a reward rj. We will define all of these key variables in the following paragraph.
The obvious choice for the observation sj would be to feed into the RL agent some representation of a noisy measurement trajectory. Then, the network would need to learn to filter the trajectory in a suitable way to learn as much as possible about what is going on inside the quantum device. However, it turns out that (i) solving this challenge simultaneously with the RL problem is surprisingly hard and (ii) we can apply another approach that is more efficient and also works well in an experiment. In fact, to solve this subtask, we can take the noisy measurement traces and use them to evolve the quantum state according to the stochastic ME. This can be performed online in a real experiment (as long as the quantum device is well characterized beforehand, and we have a sufficiently fast signal processing device, e.g. a field-programmable gate array, FPGA). Therefore, we choose to feed the result of this procedure into the RL agent, meaning we provide the quantum state ^ρ as input. Any measurement inefficiencies will be encoded in this quantum state, increasing the uncertainty and decreasing the purity. This approach has been applied successfully before, e.g. in [18, 14] . Alternatively, one can apply the idea of 'two-stage learning' introduced in our previous work [14] . In this approach, one uses a supervised learning approach to train a recurrent network, which can be deployed in an experiment as it receives only the measurement results as input. Supervised training is performed by teaching this recurrent network to mimic the output (i.e. the action sequences) produced by the original network, which was fully trained via the RL technique discussed here.
In order to avoid confusion, we note that for our case, there is no distinction between "observation" and "state". Usually an observation can be a partial representation of the state of the system, in which case we have a partially observable Markov decision process (POMDP), or it can contain the full information about the system, and in that case we have a standard Markov decision process (MDP). We will use the density matrix ^ρ as observation, such that the two definitions of state and observation coincide in this case. On the technical level, the state s fed into the neural network's input layer is a vector of length 2N2 representing all the real and imaginary parts of the entries of the cavity's density matrix. Here N is the cutoff in the Fock state basis of the cavity that we use in our simulations.
The actions aj are the real and imaginary part of the displacement drive applied to the cavity: aj=[R(β(tj)),I(β(tj))].
Figure 4: Preparation of Fock state superpositions, via additional measurement channel control. a) Example of a trajectory for the |1⟩+|3⟩√2 target state (probability distribution, top, and drive, bottom). In the four lower plots, the shaded traces represent the ON-OFF control of the measurement channels corresponding to Fock states |0⟩ to |3⟩. The solid lines are a moving average over 10 timesteps. b) Average fidelities reached after the training for different superposition states. For each case, we show the distribution of final fidelities (white histogram, obtained from 50 trajectories) and the resulting average fidelity. Above that panel, we show the comparison between the Wigner densities for the target state (top) and the average state obtained from the strategy (bottom). Smaller distance between the two states forming the superposition can yield better results. c) Average Wigner densities reached by RL, with different relative phases between state |1⟩ and |3⟩. In all cases, we only control the first M measurement channels, with M=4.
Later, the actions will be extended to accommodate control of the measurement rate in the various channels. The actions are predicted at each timestep by a policy neural network, which receives the current density matrix of the system as its input.
In the version of continuous-action RL we are using here, each neuron in the output layer represents the mean of a Gaussian distribution from which the actions are extracted, while the variance stays fixed. After the predicted value is extracted, the actions get manipulated by the agent according to the particular problem at hand. In our case, the components of the action represent the real and imaginary parts of the displacement drive, but they are limited to a range [−1,+1]. We rescale them by a fixed factor βmult and the resulting drive is applied to the system for the current time step.
The reward function is a critical choice in RL scenarios. It encourages the RL agent to find a robust policy that, in our case, drives the physical system to the desired target state. In this work, we chose as instantaneous reward a function of the fidelity F(t), computed between the current density matrix ^ρ(t) and the target state ^ρtarget(t):
rt=|F(t)|θ (8)
where the usual definition of quantum fidelity F(t)=(tr[√√ρ(t)ρtarget√ρ(t)])2 is used. We introduced an exponent θ>1 because we want to punish states which only have a moderately high fidelity (e. g. F(t)=1/2 when |ψ⟩=|n⟩ and |ψtarget⟩=1√2(|n⟩+|m⟩) ) in favour of states with fidelity much closer to 1. In our numerical experiments, it turned out that this modified reward was crucial to the success of the algorithm. Unless otherwise specified, in the following we will use θ=8. As usual, the total return R of a trajectory is then the sum of all instantaneous rewards, i.e. R=∑trt.
As mentioned before, the RL agent is modelled with a neural network, which is trained in order to maximize the cumulative reward in a trajectory. We will refer to the network as Policy Network. In actor-critic RL methods, such as the one we will be using here, this network is supplemented by a Value Network, which is used to predict the expected cumulative reward if one starts in the current state, serving as a baseline. Specifically, as our RL approach we decided to use a Proximal Policy Optimization (PPO) algorithm [45] , which is known to be a modern, general, easy-to-implement and sample-efficient variant of policy gradient techniques. This algorithm is closely related to Trust Region Policy Optimization (TRPO) [46] techniques, in the sense that they rely on updating the current policy according to some constraints, limiting sudden jumps in the latter (see figure 2 and the appendix for the general layout of the algorithm). Even though this is a time-dependent control problem, using recurrent neural networks, i.e. networks with memory, is not necessary in our approach: knowing the quantum state at any time gives the maximum amount of information that is available and that can possibly influence the choice of the next action. The output of the Policy Network, for each action, is a Gaussian distribution from which the action is extracted. The deep RL agent, during the training, learns the peak location of the Gaussian while the standard deviation is kept fixed. This strategy is useful during the training to explore the action space, and naturally deals with stochastic environments, like the one in study. RL techniques for efficient control with continuous action spaces are a relatively recent development. For the present work, we perform the training of the RL agent using the Python library Stable Baselines [47] .
Figure 5: Analysis of the strategy discovered by deep RL. a) Response of different neural networks trained on target states |1⟩ ,|1⟩+|3⟩√2, |3⟩, respectively. To visualize the output of the neural network as a function of its input, the input state is parameterized with two variables (x,y): x|1⟩+√1−x2−y2|2⟩+y|3⟩; thus the stars in each plot represent the corresponding target state (up to a global phase). For each panel, we show the real value of the displacement drive β(t), while the imaginary part is negligible (averaging the ouputs of 7 different converged networks). b) The measurement channel actions discovered for the |1⟩+|3⟩√2 target state, plotted in the same way as before vs parametrized input state. c) Average final fidelity as a function of measurement rate γmeas and maximum drive amplitude βmax (for the example |ψtarget⟩=|3⟩). Comparison between the greedy strategy (see main text) and Reinforcement Learning. RL is more robust in a wider range of physical parameters of the system.
V Results
Many different stochastic trajectories (or "episodes"), all starting from the ground state |ψ⟩=|0⟩, are run in parallel, then the data are collected, and the two networks, the policy and the value neural network, are updated. Unless otherwise specified, in our numerical experiments each trajectory is made up of 1000 timesteps. At each timestep, the agent inputs the current density matrix to the policy neural network, it extracts real and imaginary parts of the displacement, and it applies that displacement drive to the cavity.
In Fig. 3, we show the main results for our RL agent that was trained on reaching different Fock states, from |ψtarget⟩=|1⟩ to |ψtarget⟩=|7⟩. All of these results are still in the absence of decay or dephasing, except for the unavoidable, intrinsic measurement-induced dephasing processes described by the SME. We will later return to verify the performance of the RL approach when this restriction is relaxed.
Starting from the ground state, the network learns to displace to reach higher energies. At the same time, the measurement has two tendencies: it introduces fluctuations but it also tries to collapse onto some random Fock state (not necessarily the desired target state!). The network eventually finds a strategy to compete with and exploit the measurement process, ending up in the target state and stabilizing that state. The RL agent reaches high fidelities successfully after seeing only about 2000 trajectories, which is surprisingly efficient in the context of deep RL. In our case we explored quantum state preparation until a target Fock state number 7 without any noticeable loss of fidelity.
The resulting strategies obtained by the agent surpass even the best benchmark strategy that we could construct based on our physical intuition, a greedy strategy sketched in Appendix 7. Essentially, this greedy strategy attempts, at each time, to choose a displacement that would maximize the overlap with the target state.
Now we move on to a more challenging problem, where the target states become superpositions of Fock states. This is in principle incompatible with continuously active Fock state measurements, because these will ultimately destroy any such superposition. We therefore allow an increase in capabilities for the nonlinear measurement, enabling the RL agent to control the measurement rates for the various channels. This is possible in a real experiment by controlling the amplitudes of the microwave tones applied to the system. In principle, the RL algorithm could handle without any problems the extension to M additional continuous actions, representing the measurement rates. We decide to go for a simplified approach, in order to better understand the resulting strategy after the training: in this simpler approach, χn of Eq. 1 can only be 0 or 1, meaning that each of the first M channels (corresponding to state |0⟩ to |M−1⟩ are respectively either OFF or ON at any moment of time. Therefore, in Fig. 4 the actions are M+2 dimensional: real and imaginary part of the displacement drive and M ON-OFF measurement rates. In Fig. 4a we visualize the strategy chosen by the RL agent, for the target state (|1⟩+|3⟩)/√2. In the beginning of the trajectory, the values of the measurement rates are not critical, so the agent just learns to displace the ground state with the maximum displacement available. Then, in the middle of the trajectories, the strategy relies on a counter-play between the measurement-induced collapse of the state and the effect of the drive. The agent finds out that the best strategy is to keep channels 0 and 2 ON (measurements on Fock states |0⟩ and |2⟩) while keeping channel 3 completely OFF. Instead, channel 1 is kept OFF when the state has collapsed to the correct one, while on average is ON in the middle of the trajectory, possibly in order to avoid the broadening of the state. In Fig. 4b, the final states reached are plotted in comparison to the true target states. For superpositions between Fock states that are further apart, it is harder to reach large fidelities. However, it is worthwhile to notice that only the first 4 channels are controlled in these examples, in addition to the already limited linear drive controls. From this perspective, it is still remarkable how well even superpositions can be produced (as also evidenced by the Wigner densities).
Vi Analysis and robustness of the network
Figure 6: Robustness of the Reinforcement Learning approach vs. decay and dephasing (or measurement inefficiency). The target state in this example is |3⟩. a) Average final fidelity vs either dephasing rate or decay rate; the dashed line represents the fidelity without noise (compare Fig. 3). Each data point represents the strategy of a freshly trained neural network. b) Average fidelity when both decay and dephasing are present, in a 2D parameter sweep.
In order to analyze the behaviour of the trained agent, we study the relation between input (state) and output (action) that the policy network has learned after the training is completed. Our first analysis inquires how the network trained on a particular target state behaves when it receives a state which slightly differs from the target. In Fig. 5a we show the input-output relations of three different trained neural networks, trained to reach states |1⟩ ,|1⟩+|3⟩√2, |3⟩ respectively. The real part of the displacement is shown, plus the measurement controls in the superposition case. As expected, when the input corresponds to the target state, all neural networks output zero drive, since the correct state has been reached. Interestingly, when the target state is a pure Fock state, the neural network decides to displace only slightly (i.e. β(t)∼0 ) when the input state is a superposition between the target state and another one. In Fig. 5a we show the measurement strategy when the target is a superposition, |1⟩+|3⟩√2. The network measures consistently state |2⟩, independently from the input state, matching what was already shown in Fig. 4.
It is equally interesting to analyze the dependence of the discovered strategy on the physical parameters, i.e. the measurement rate Γmeas and the maximum available drive strength βmax. The results are displayed in Fig. 5c. While we sweep through different parameter values, each network is trained completely anew by our RL approach, but in principle one could start from an already trained network on a different set of parameters and use that as initialization for the new training. This strategy would save computation time but its adoption would not change the results shown in Fig. 5. We observe that the RL approach yields high fidelities once both the maximum allowed drive and the measurement rate exceed a certain threshold. Specifically, both the drive βmax and γmeas need to be multiplied by the total time span Tmax alloted for the control task, and the resulting dimensionless numbers need to cross a certain threshold for each of these quantities, as seen in the figure. In addition, we have compared the RL results with the greedy algorithm (sketched in Appendix 7). It can be readily seen in Fig. 5b that the RL algorithm can reach higher average fidelities in a wider ranges of parameters, and performs slightly worse only when the maximum displacement allowed is quite small.
Finally, we turn our attention to checking the robustness of the reinforcement learning strategies to different kinds of noises. Specifically, we implemented two different disturbances of the system: decay and dephasing. The decay is modelled with an additional term in the master equation 6 of the form γdecayD[^a]. Dephasing is covered by a term γdephasing2∑nD[^Pn]. We note that this additional dephasing can also be seen as describing an inefficient measurement, without any corresponding increase of information (this results in an impure quantum state). The results of this analysis are shown in Fig. 6, where the RL approach has been used to discover strategies for different amounts of decay and dephasing. The RL strategy can perform well even when both kinds of noises are present in the system, and it outperforms the greedy strategy. While the deleterious influence of dephasing is comparatively small, the effects of decay seem to be significantly more severe (when comparing results at the same rate). This insight provides an important guideline for future experiments.
The present work has demonstrated the feasibility of using deep RL to compensate limited control by nonlinear measurements in an experimentally relevant setting. However, it also has underlined the importance of fundamental questions about controllability under feedback: in our scenario, under some circumstances, RL can only reach a limited fidelity, but is this a necessary consequence of the limited control or could it be overcome by better strategies that the adopted RL approach simply did not discover?
The experimental implementation of the strategies discovered here, for quantum state preparation in a cavity, will rely either on fast on-chip processing to extract the quantum state update from the observed measurement signal or will exploit the general idea of two-stage learning, training a deployable network in a supervised fashion after RL discovery of the strategy has succeeded. It would be equally interesting to see whether direct RL training starting from the measurement trajectories obtained in the experiment could work, although that will likely require improved algorithms.
In the future, the approach adopted here could be applied to other scenarios such as cavities with nonlinearities where the control consists either in a drive (as in the present work), in a detuning, or even in a time-dependent control of the strength of the nonlinearity. These control strategies are well suited to quantum error correction based on bosonic codes [48] , and could thus be instrumental in the development of a fault tolerant quantum processor. The extension to quantum many-body scenarios, like multiple coupled nonlinear cavities or qubits, seems equally promising. In all these cases, it would be hard to find suitable quantum feedback strategies without the help of a tool as powerful as deep RL.
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Figure 7: Strong measurement-based strategy applied for first 7 Fock states. The probability of success is not monotonic for the first two iterations, then decreases. The cut-off Fock state is |70⟩, since we empirically proven that the probability of displacing higher states to obtain a Fock state with n<7 will basically converge to 0. It is worth noticing that the cumulative sun of these probabilities, for each target Fock states, never goes above 0.91, denoting a probability of not succeeding with this strategy in a finite time which is close to 9%.
In the Fock state basis, the displacement operator acts on |n⟩ like [49, 50] :
D(α)|n⟩=exp[−|α|2/2]∞∑k=0αkk!n∑j=0(−α∗)jj![(n−j+k)!n!(n−j)!(n−j)!]1/2|n−j+k⟩. (9)
Ideally, to drive a system from |n⟩ to |l⟩, one wants to maximize:
⟨l|D†αρDα|l⟩ (10)
with ρ=|n⟩⟨n|. By introducing two identities:
⟨l|D†αρDα|l⟩=∑l′,m′⟨l|D†α|l′⟩⟨l′|ρ|m′⟩⟨m′|Dα|l⟩ (11)
So we need to compute ⟨l|Dα|n⟩ for a generic n and l.
⟨l|Dα|n⟩ = exp[−|α|2/2]n∑j=0∞∑k=0αkk!(−α∗)jj![(n−j+k)!n!(n−j)!(n−j)!]1/2δl,n−j+k⟨l|n−j+k⟩ (12)
= exp[−|α|2/2]n∑j=max(0,n−l)α−n+j+l(−n+j+l)!(−α∗)jj!(n−j)!√l!n! (13)
= exp[−|α|2/2]√l!n!α−n+ln∑j=max(0,n−l)|α|2j(−n+j+l)!j!(n−j)! (14)
Plugging this into Eq. 11,and assuming ρ=|n⟩⟨n|:
∑l′,m′⟨l|D†α|l′⟩δl′,n⟨l′|n⟩δn,m′⟨n|m′⟩⟨m′|Dα|l⟩=⟨l|D†α|n⟩⟨n|Dα|l⟩=∣∣⟨l|D†α|n⟩∣∣2 (15)
∣∣⟨l|D†α|n⟩∣∣2=∣∣ ∣∣exp[−|α|2]l!n!α2(l−n)n∑j=max(0,n−l)|α|2j(−n+j+l)!j!(n−j)!∣∣ ∣∣2 (16)
As example, we can compute this overlap with |ψi⟩=|i⟩=|0⟩ and |ψf⟩=|l⟩=|2⟩, obtaining:
⟨2|Dα|0⟩⟨0|D†α|2⟩=12e−α2α4 (17)
We now can compute the optimal α that maximizes the overlap between any initial state |n⟩ and target |l⟩, namely αoptim(n,l). After computing this table of values, we can develop a strategy to achieve a particular target Fock state |l⟩. The idea is is to start from n=0 (i.e. the ground state) and then we displace by the optimal αoptim(0,l). Then, we strong measure the evolved state, and this will collapse to a particular Fock state |l1⟩. If l1=l, then the algorithm ends and the trajectory was successfull. If, instead, l1≠l , we displace |l1⟩ by the optimal αoptim(l1,l) and we iterate this project until we reach |l⟩ or the algorithm reaches a very high Fock state (discussed in 7). We use this strategy for each target Fock state from n=1 to n=7, as shown in 7. We see that the probability of reaching the target state is very high in the first few iterations, while it slowly decreases otherwise.
Now we will try to find an optimal strategy that doesn't assume Fock states as target states This is referred in the main text as "greedy strategy".
The system starts in the initial state |ψi⟩. Then, this states is evolved though the master equation describing the system in hand, but with every possible value of the displacement D(α). We discretise the values of α in N possibile values, therefore obtaining N different target states after evolving |ψ⟩(t) to |ψ⟩(t+Δt). Among these N states we take the one that maximeses the fidelity with the target state that we want, i.e. |ψf⟩. We then repeat this procedure for every timestep in a trajectory. This strategy is referred to as "greedy", since the best possible strategy could not pass through local optima, but in some cases an optimal strategy would rely in a completely different path.
Appendix B: PPO
PPO [45] is an on-policy algorithm, meaning that the policy is updated directly from the data and it doesn't need to collect them in order to update it, like off-policy algorithm do. This means that PPO could be used directly attached to an experimental setup, as opposed to off-policy algorithms (like Q-learning). The main idea behind PPO is a policy gradient method, which consists in computing an estimator of the policy gradient and then plugging it in a stochastic gradient ascent algorithm. Such estimator is defined as:
L(θ)=^Et[logπθ(at|st)^At] (18)
where πθ(at|st) is the probability of choosing action at, given state st and according the policy parameters θ. ^At is called advantage function and estimates the value (i.e. the cumulative reward from timestep t onwards) of action at. ^At is then composed of two terms:
^At=tmax∑t′=tγt′rt−^V(st) (19)
where the first one represents the (discounted) some of rewards (i.e. discounted return) from timestep t and the second one a value function that estimates the discounted reward. If ^At>0 this means that action at would collect more reward as expected and the agents needs to select it more often in future episodes. To estimate ^V(st) we use a second neural network, which takes state st as input, but one could use in principle only the policy neural network and branch the last layer and adding one additional continuous-valued neuron, exploiting the already processed state after the first layers of the network. The problem with estimator18 is that multiple steps of optimization using the same trajectory could bring to potentially too big policy updates. The idea of PPO is to optimize an objective function that constrains its update. This objective function is defined as:
LPPO(θ)=^Et[min(rt(θ))^At,%clip(rt(θ),1−ε,1+ε)^At)] (20)
where rt(θ) is defined as:
rt(θ)=πθ(at|st)πθold(at|st), (21)
θold are the weights of the policy before the update and ε is an hyperparameter.


text:
Se7en or SE7EN may refer to:

 Seven (1995 film), stylized as Se7en, 1995 American thriller film
 Seven (comic book), stylized as SE7EN, 2008 comic book based on the film
 Se7en (born 1986), stage name of singer Sevyn Streeter while in the band RichGirl
 Seven (South Korean singer), stylized as Se7en (born 1984)
 David Gallegos, rapper and screamer of the crunk group Brokencyde
 Damien Broman Se7en, one of the stage names used by rapper Silver Bullet
Headshots: SE7EN, a 2005 remaster compilation album by the band Atmosphere

See also
7 (disambiguation), also includes Seven

redpajama_set_name: RedPajamaWikipedia


text:
Q: FromDigits of list of list Instead of
FromDigits[{2, 1, 1}, 2]

with output 11, I want to insert a whole list of lists instead of {2,1,1}.
So if
lis = {{2, 1, 1}, {1, 1, 1}, {1, 0, 0}}
I want as output: {11, 7, 4} but FromDigits[lis, 2] does not work. How can I do it?

A: The list of lists is read column-wise, so you need to transpose lis:
FromDigits[Transpose[lis], 2]

{11, 7, 4}

If the list is ragged then FromDigits[Transpose[PadRight[lis]], 2]

redpajama_set_name: RedPajamaStackExchange


text:
SM Entertainment Releases Statement On Red Velvet Wendy's Condition And Future Plans
Finally, some good news.
Red Velvet's Wendy was involved in an accident last year that broke the hearts of many fans. Eight months later, she is finally ready to appear in front of fans again.
Last night, with the release of her teaser image for BoA's 20th Anniversary cover project by SM Entertainment, Wendy set social media ablaze. Fans trended multiple hashtags for the lovely singer and she began trending on real time, as everybody wondered if it meant she was back on scene.
| SM Entertainment
This morning, we have our answer. On the 19th of August, SM Entertainment reportedly told media outlets, including TV Daily, that Wendy has recovered plenty and would be participating in future activities.
Their short but succinct statement reads,
Wendy's status is that she is now currently in a condition that has recovered a lot. We plan for her to be participating in Red Velvet's activities, within a capacity that does not strain her.
— SM Entertainment
Since her accident in December 2019, Wendy has been focusing on her own recovery and recuperation. It was previously reported that the girls of Red Velvet would stop in to take care of her and her parents had also come to Korea to be with her.
In other news, Wendy had also donated ₩100 million KRW ($84,416 USD) to victims of the coronavirus in February, proving her kind heart.
Source: TV Daily
Red Velvet's Irene Tells "Esquire Korea" How Much She Misses Fans
Why Red Velvet's Joy Thinks Her Photos Come Out Best When Her Armpits Are In Clear View
These Are The Two Most Important Things That Red Velvet's Yeri Needs To Be Happy
Red Velvet's Joy Has A Secret Flirting Technique, And It's Honestly Genius
See more Red Velvet


text:
Home » Music » The Early Comebacks: Pass You By
The Early Comebacks: Pass You By
The Early Comebacks are back with their sun-drenched new track 'Pass You By'. The indie rock duo from California have found a dedicated fan-base with their brand of West Coast, sepia-toned rhythms. Impressively, the music was all written and performed by vocalist Kevin Ty in his Bay Area home studio – with the exception of Kevin Coleman's drums. Ty has been deservedly compared to other musical innovators such as Toro y Moi and Car Seat Headrest's Will Toledo, his music blending the best elements of 60's pop and 00's indie to create a sound that embodies the warmth of his native California.
'Pass You By' is taken from the upcoming EP 'Distant Distance'; a collection of songs crafted with "heavy doses of love, caffeine and introspection" Ty explains. From the offset, 'Pass You By' lulls the listener into a false sense of security. The disarming country-esque guitar and smoothing cello is swiftly interrupted as the drums break into a driving rhythm, akin to the style of bands like Phoenix. The cushy production is juxtaposed with twists and turns in melody throughout the song and, in much the same way, the song also displays layers of depth lyrically. When an echoing, spiraling lick first introduces the vocals, we meet a free-loving character who is "too busy" for his love interest.
However, there is inevitably a sense of aching; no matter how subdued in nature it seems – "I hope my messages came through 'cos I've been waiting to hear from you." This is one of the Early Comebacks' most endearing qualities, their balance of confession and confrontation. They manage to be heartfelt without being overly emotive. There is a sense of nostalgia without too much sentiment, and the music is never second to the narrative. Warmth radiates off this track with its cacophony of lush melodies and unvarnished guitar and ultimately this is a song of recollection, of bygone days; a piece of music that can't help but evoke fuzzy memories like old home video footage. The Early Comebacks are most certainly a welcome return with 'Pass You By'.
The Early Comebacks: Pass You By Lyrics:
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Gov. Janet Mills announced the financial incentive program in a news release March 21. Funding comes from a 2017 award Maine received as part of a multistate settlement with carmaker Volkswagen to resolve environmental damage caused by that company's cheating U.S. emissions tests.
"Maine people shell out $5 billion a year to out-of-state fossil fuel companies, and a lot of that money is spent on gas for vehicles that just becomes carbon dioxide pumped into our atmosphere," Mills said in a statement.
About half the money will be used to help public agencies and organizations that serve elderly, low-income people and people with special needs buy electric vehicles. The remainder will provide rebates to Maine residents and businesses to buy or lease electric vehicles.
It is unclear how the incentives and rebates will be awarded.
The state estimates rebates could put up to 900 new electric vehicles on Maine roads, more than twice the number now registered in the state.
Efficiency Maine Trust in April plans to ask for bids to install 50 to 60 electric vehicle charging stations at public buildings across the state.


Each day, we have a wide variety of members walk through the doors of our four Fit Factory Locations. Some are elite athletes looking to stay on top of their training, some are young professionals looking to de-stress from work with a workout, some are just beginning their fitness journey coming in to meet with a personal trainer, and some are somewhere in between all of those things. Everyone's story is so different, and that's our favorite part! We love learning about YOU and why you decided to join the Fit Fam. We love hearing all that you've accomplished. We love listening to all of the new goals you've set for yourself, and being here to watch you acomplish and exceed them. You and your unique stories is exactly what makes Fit Factory so special.
"I have always had gym memberships but never really used it. Fit Factory, Braintree changed this for me. Specifically, the group fitness classes. I started coming to this branch of fit factory around september 2018. Today, almost 2 and half months later I have lost 18 pounds. I feel the healthiest and strongest I have ever felt.
GROUP FITNESS class is EVERYTHING!
Zumba class is my favorite, both Kelsey and Sarah are absolutely fantastic. Class is fun, fast paced and you will definitely get out of the class drenched in sweat feeling exhilarated. Dance routine is easy to catch on but challenging enough it will keep you coming back for more.
Some of the other classes I love are the total body burn (Sarah), Kickboxing (Kelsey), HIIT (Alli and Kelsey), Barre (Kelsey) and Yoga (Rebecca, Danielle, Sarah). HIIT classes are a must if you are looking to lose weight and get strong at the same time. Special shoutout to Kelsey (Group Fit coordinator)-Kelsey is a total Boss Lady and has done an epic job putting a great team of instructors together. I want to thank Kelsey and her team for changing my life for good. I am going into 2019 feeling Fit and Fabulous :)"
Thanks for being a part of the Fit Fam, Diya! We can't wait to see what you accomplish in 2019!


Perhaps you and most of your friends make under $100,000 per year. You have a goal to increase your income to $1,000,000 per year. If you succeed, your lifestyle will probably change, and this will likely put a strain on many of your friendships. Foam stress balls have transformed into stress toys, coming in numerous shapes and themes. You could squeeze animals, birds, humans, bugs and many other shapes. It could even be a boss shape better whacked than squeezed.
9)	E-commerce tool with over 100 advanced searches to build your business There are many ways to drive traffic to your site, both free and paid. But we are not going to talk about paid traffic right now. That is a totally different topic and could take hours to cover.
The key is finding the way that works for you. The cost of the printer and the ink cartridges. With current sales revenue of 25 million USD, the company expects to reach a turnover of close to 150 million USD within five years. It can be made in desired shapes, color and texture. The good thing about it is that you don?t actually need to produce it yourself. Browse to your images, upload them and put them in the categories they offer.
You are overweight and so is your spouse, and the two of you eat a lot of fattening foods together. There are work from home sites offering a simple method to make thousands of dollars per week in just a few days or a few weeks.
43 years old Saw Machine and Repairer Hutton from Oromocto, has lots of hobbies including ceramics, , and poker. Gains encouragement by planning to Historic Centre of Sighisoara.


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HomeNewsImport - Export newsVietnamese win Global Industrial Competition
Written by Super User on 28 July 2013 . Posted in Business news
Doosan Heavy Industries Vietnam announced that Vitenamese technicians had recently won a global industrial competition in Korea. The competition involved entries from Doosan Heavy Industries seven worldwide production sites and to honor and welcome the winning team home a cermony was held at the company's 110 hectarce high tech manufacturing complex in Quang Ngai.
"This is a significant event because Vietnamese industrial skills were pitted against much more experienced and senior operations from around the world, and we won!" Said a spokesperson who went on to say, Doosan's Vietnamese operation has only been in production for a little more than three years and winning the competion is certanily a testimony to the skill and expertise of the Vietnamese workforce".
The twelve members of the "Panel Fabrication One Team" of the Boiler Division were given a heroes welcome and honored by the Board of Management and the entire staff of the Boiler division with speeches, banners, flowers, a three day victory tour of Vietnam and a party with a special cake made in thier honor.
The winning Vietnamese entries were a chamfering tool and mini crane that the team created to help overcome some production issues in the manufacture of the high tech boilers which are the heart of a modern power plant.
The new chamfering tool & mini crane helped save nearly 2,000 man hours and 1,000 hours of crane time on the MD II boiler being fabricated by Doosan Vina and these innovations will continue to be time savers on all future Boiler projects.
"The victory at the Doosan-wide Keson Contest was really an unforgettable experience. It is remarkable that in three years Doosan Vina's innovation is comparable to that of our parent companies 30-year history, I am flying high, because after all the research and testing, we succeeded and have shown the exceptional creativity of Vietnamese workers," Chief Worker, Phan Thanh Phat said.
Doosan Vina
Doosan Vina is a high tech industrial complex in the Dung Quat Economic Zone of Central Vietnam's Quang Ngai Province. The company supplies the mega infrastructure products that make modern life a reality. Doosan Vina's products include: Boilers for thermal power plants, Heat Recovery Steam Generators that increase the efficiency of a typical power plant by over 30%, Desalination plants the size of a football pitch that turn sea water into fresh, Material Handling Systems like the cranes that are the heart of logistics at ports around the world and Chemical Processing Equipment that turns the earth's natural resources into the useful products we use every day.
The Doosan Group
The Doosan Group is a global multinational focusing on Power, Water and other infrastructure developments worldwide. The company is headquartered in Seoul, South Korea, has operations in 37 countries, over 35,000 employees and $22 billion in annual revenue.
The winning team was welcomed by the Board of Management and the entire staff of the Boiler shop for their championship win at the global Doosan-wide Keson industrial production contest held in Korea.
VietnamShipper