A selection of the most important recent news, articles, and papers about quantum computing.
News, Articles, and Analyses
Meet Majorana | WIRED
https://www.wired.com/sponsored/story/meet-majorana/
(Monday, June 03, 2024) “WIRED Brand Lab | For decades, we’ve heard that quantum computing will be the future. Well, if this little particle has anything to do with it, the future is now.”
New Oxford quantum hub to tackle key challenges in quantum technologies | University of Oxford
(Friday, July 26, 2024) “Today, the UK Government has announced the launch of five new research hubs to develop quantum technologies in areas ranging from healthcare and computing to national security and critical infrastructure. One of the hubs will be led by the University of Oxford, and aims to develop the technologies needed for the UK to play a key role in the development of quantum computers – a”
IonQ to Report Second Quarter 2024 Financial Results on August 7, 2024
“IonQ (NYSE: IONQ), a leader in the quantum computing industry, today announced that the company will release its financial results for the quarter ended June 30, 2024, on Wednesday, August 7, 2024, after the financial markets close. IonQ will host a conference call at 4:30 PM Eastern time that same day to discuss its results and business outlook. The call will be accessible by telephone at 877-407-4018 (domestic) or 201-689-8471 (international). The call will also be available live via webcast on the Company’s website here , or directly here . A telephone replay of the conference call will be available approximately three hours after its conclusion at 844-512-2921 (domestic) or +1-412-317-6671 (international) with access code 13746744 and will be available until 11:59 PM Eastern time, August 21, 2024. An archive of the webcast will also be available here shortly after the call and will remain available for one year. About IonQ IonQ, Inc. is a leader in quantum computing that delivers”
D-Wave Quantum to Report Second Quarter 2024 Financial Results on August 8, 2024
“D-Wave Quantum Inc. (NYSE: QBTS) (“D-Wave”), a leader in quantum computing systems, software, and services, today announced it will release its financial results for the second quarter of fiscal year 2024 ended June 30, 2024 on Thursday, August 8 before market open. The press release will be available on the D-Wave Investor Relations website: https://ir.dwavesys.com/ . In conjunction with this announcement, D-Wave will host a conference call on Thursday, August 8, 2024, at 8:00 a.m. (Eastern Time), to discuss the Company’s financial results and business outlook. The live dial-in number is 1-800-717-1738 (domestic) or 1-646-307-1865 (international). Participating in the call will be Chief Executive Officer Dr. Alan Baratz and Chief Financial Officer John Markovich. About D-Wave Quantum Inc. D-Wave is a leader in the development and delivery of quantum computing systems, software, and services, and is the world’s first commercial supplier of quantum computers—and the only company”
Why the Quantum Industry needs an Apollo program
https://substack.com/home/post/p-147028527
Author: Petra Soderling
“This past Wednesday, I recorded an episode with Dr. Bob Sutor for Deep Pockets podcast. Wednesday was July 24th. On that same day, in 1969, Apollo 11 astronauts Neil Armstrong, Buzz Aldrin, and Michael Collins landed in the Pacific Ocean aboard the Command Module Columbia, wrapping up their eight-day mission to the Moon and back.”
Technical Papers and Preprints
[2007.00958] Quantum simulation with hybrid tensor networks
https://arxiv.org/abs/2007.00958
Authors: Yuan, Xiao; Sun, Jinzhao; Liu, Junyu; Zhao, Qi; Zhou, You
(Thursday, July 02, 2020) “Tensor network theory and quantum simulation are respectively the key classical and quantum computing methods in understanding quantum many-body physics. Here, we introduce the framework of hybrid tensor networks with building blocks consisting of measurable quantum states and classically contractable tensors, inheriting both their distinct features in efficient representation of many-body wave functions. With the example of hybrid tree tensor networks, we demonstrate efficient quantum simulation using a quantum computer whose size is significantly smaller than the one of the target system. We numerically benchmark our method for finding the ground state of 1D and 2D spin systems of up to $8\times 8$ and $9\times 8$ qubits with operations only acting on $8+1$ and $9+1$ qubits,~respectively. Our approach sheds light on simulation of large practical problems with intermediate-scale quantum computers, with potential applications in chemistry, quantum many-body physics, quantum field theory, and quantum gravity thought experiments.”
[2407.17659] Discretized Quantum Exhaustive Search for Variational Quantum Algorithms
https://arxiv.org/abs/2407.17659
(Wednesday, July 24, 2024) “Quantum computers promise a great computational advantage over classical computers, yet currently available quantum devices have only a limited amount of qubits and a high level of noise, limiting the size of problems that can be solved accurately with those devices. Variational Quantum Algorithms (VQAs) have emerged as a leading strategy to address these limitations by optimizing cost functions based on measurement results of shallow-depth circuits. However, the optimization process usually suffers from severe trainability issues as a result of the exponentially large search space, mainly local minima and barren plateaus. Here we propose a novel method that can improve variational quantum algorithms — “discretized quantum exhaustive search”. On classical computers, exhaustive search, also named brute force, solves small-size NP complete and NP hard problems. Exhaustive search and efficient partial exhaustive search help designing heuristics and exact algorithms for solving larger-size problems by finding easy subcases or good approximations. We adopt this method to the quantum domain, by relying on mutually unbiased bases for the $2^n$-dimensional Hilbert space. We define a discretized quantum exhaustive search that works well for small size problems. We provide an example of an efficient partial discretized quantum exhaustive search for larger-size problems, in order to extend classical tools to the quantum computing domain, for near future and far future goals. Our method enables obtaining intuition on NP-complete and NP-hard problems as well as on Quantum Merlin Arthur (QMA)-complete and QMA-hard problems. We demonstrate our ideas in many simple cases, providing the energy landscape for various problems and presenting two types of energy curves via VQAs.”
[2407.18202] Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning
https://arxiv.org/abs/2407.18202
(Thursday, July 25, 2024) “The emergence of quantum reinforcement learning (QRL) is propelled by advancements in quantum computing (QC) and machine learning (ML), particularly through quantum neural networks (QNN) built on variational quantum circuits (VQC). These advancements have proven successful in addressing sequential decision-making tasks. However, constructing effective QRL models demands significant expertise due to challenges in designing quantum circuit architectures, including data encoding and parameterized circuits, which profoundly influence model performance. In this paper, we propose addressing this challenge with differentiable quantum architecture search (DiffQAS), enabling trainable circuit parameters and structure weights using gradient-based optimization. Furthermore, we enhance training efficiency through asynchronous reinforcement learning (RL) methods facilitating parallel training. Through numerical simulations, we demonstrate that our proposed DiffQAS-QRL approach achieves performance comparable to manually-crafted circuit architectures across considered environments, showcasing stability across diverse scenarios. This methodology offers a pathway for designing QRL models without extensive quantum knowledge, ensuring robust performance and fostering broader application of QRL.”