A5000073123- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
- Quantum and electron transport phenomena
- Quantum many-body systems
- Quantum-Dot Cellular Automata
- Theoretical and Computational Physics
- Quantum Mechanics and Applications
- Neural Networks and Applications
- Advanced Thermodynamics and Statistical Mechanics
- Advancements in Semiconductor Devices and Circuit Design
- Neural Networks and Reservoir Computing
- Machine Learning in Materials Science
- Random Matrices and Applications
- Quantum chaos and dynamical systems
- Advanced Chemical Physics Studies
- Advanced Data Storage Technologies
- Microtubule and mitosis dynamics
- Parallel Computing and Optimization Techniques
- Workplace Health and Well-being
- Healthcare professionals’ stress and burnout
- Protein Structure and Dynamics
- Cell Image Analysis Techniques
- Genetics, Bioinformatics, and Biomedical Research
- Colorectal Cancer Treatments and Studies
- Algebraic structures and combinatorial models
University Medical Center Freiburg
2022-2025
University of Freiburg
2022-2025
Google (United Kingdom)
2024
DeepMind (United Kingdom)
2022-2024
University of Cambridge
2016-2023
Phasecraft Ltd. (United Kingdom)
2021-2023
Google (United States)
2022
Cornell University
2013
Mappings between fermions and qubits are valuable constructions in physics. To date only a handful exist. In addition to revealing dualities fermionic spin systems, such mappings indispensable any quantum simulation of physics on computers. The number required per mode, the locality mapped operators strongly impact cost simulations. We present novel fermion qubit mapping which outperforms all previous local both mode ratio, operators.
Recurrent neural networks are the foundation of many sequence-to-sequence models in machine learning, such as translation and speech synthesis. In contrast, applied quantum computing is its infancy. Nevertheless there already exist learning variational eigensolvers which have been used successfully e.g. context energy minimization tasks. this work we construct a recurrent network (QRNN) with demonstrable performance on non-trivial tasks sequence integer digit classification. The QRNN cell...
Abstract Building a large-scale quantum computer requires effective strategies to correct errors that inevitably arise in physical systems 1 . Quantum error-correction codes 2 present way reach this goal by encoding logical information redundantly into many qubits. A key challenge implementing such is accurately decoding noisy syndrome extracted from redundancy checks obtain the encoded information. Here we develop recurrent, transformer-based neural network learns decode surface code,...
What happens to undecidability in the quantum computing paradigm?
We examine the usefulness of applying neural networks as a variational state ansatz for many-body quantum systems in context information-processing tasks. In network ansatz, complex amplitude function is computed by network. The resulting multipartite entanglement structure captured this has proven rich enough to describe ground states and unitary dynamics various physical interest. present paper, we initiate study demonstrate that are capable efficiently representing codes information...
The spectral gap problem - determining whether the energy spectrum of a system has an above ground state, or if there is continuous range low-energy excitations pervades quantum many-body physics. Recently, this important was shown to be undecidable for spin systems in two (or more) spatial dimensions: exists no algorithm that determines general gapped gapless, result which many unexpected consequences physics such systems. However, are indications one dimensional simpler than their...
Abstract The quantum circuit model is the de-facto way of designing algorithms. Yet any level abstraction away from underlying hardware incurs overhead. In this work, we develop algorithms for Hamiltonian simulation "one below” model, exploiting control over qubit interactions available in most and deriving analytic identities synthesising multi-qubit evolutions two-qubit interactions. We then analyse impact these techniques under standard error where errors occur per gate, an with a...
Quantum state preparation is an important ingredient for other higher-level quantum algorithms, such as Hamiltonian simulation, or loading distributions into a device to be used e.g. in the context of optimization tasks machine learning. Starting with generic "black box" method devised by Grover 2000, which employs amplitude amplification load coefficients calculated oracle, there has been long series results and improvements various additional conditions on amplitudes loaded, culminating...
Abstract Background Depression associated with occupational stress is highly prevalent, causing high rates of sick leave and thus posing significant societal economic burden. Meta-analyses the few studies on psychological work-focused interventions for common mental disorders including depression report small effects depressive symptomatology outcomes. There an urgent need more controlled work-directed assessing work Methods This interventional, multicentre, active-controlled,...
Linear combinations of chi square random variables occur in a wide range fields. Unfortunately, closed, analytic expression for the probability density function is not yet known. Starting out from an sum two gamma variables, computationally efficient algorithm to numerically calculate linear combination developed. An explicit error bound obtained. The proposed technique shown be efficient, i.e. only polynomial growth number terms compared exponential most other methods. It provides vast...
We prove that estimating the ground state energy of a translationally invariant, nearest-neighbour Hamiltonian on 1D spin chain is $$\textsf {QMA}_{{\textsf {EXP}}}$$ -complete, even for systems low local dimension ( $$\approx 40$$ ). This an improvement over best previously known result by several orders magnitude, and it shows spin-glass-like frustration can occur in invariant quantum with comparable to smallest-known non-translationally similar behaviour. While previous constructions such...
In the near-term "NISQ"-era of noisy, intermediate-scale, quantum hardware and beyond, reliably determining quality devices becomes increasingly important: users need to be able compare them with one another, make an estimate whether they are capable performing a given task ahead time. this work, we develop release advanced benchmarking framework in order help assess state art current devices. Our testing measures performance universal hardware-agnostic way, metrics that aimed facilitate...
Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into logical qubit, where the rate is suppressed exponentially as more are added. However, this exponential suppression only occurs if below critical threshold. In work, we present two surface code memories operating threshold: distance-7 and distance-5 integrated with real-time decoder. The of our larger memory factor $\Lambda$ = 2.14 $\pm$ 0.02 when increasing distance two,...
Feynman's circuit-to-Hamiltonian construction connects quantum computation and ground states of many-body systems. Kitaev applied this to demonstrate QMA-completeness the local Hamiltonian problem, Aharanov et al. used it show equivalence adiabatic circuit model. In work, we analyze low energy properties a class modified Hamiltonians, which include features like complex weights branching transitions. For history with linear clocks weights, develop method for modifying propagation implement...
Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 20 May 2020Accepted: 30 March 2021Published online: 26 August 2021Keywordsquantum information theory, quantum channel capacities, computational group error correction, thresholds, actionsAMS Subject Headings81P45, 81P70, 94A40, 05E18Publication DataISSN (print): 0097-5397ISSN (online): 1095-7111Publisher: Society for Industrial and Applied MathematicsCODEN: smjcat
Recent work has demonstrated the existence of universal Hamiltonians—simple spin-lattice models that can simulate any other quantum many-body system to desired level accuracy. Until now, proofs universality have relied on explicit constructions, tailored each specific family Hamiltonians. In this work, we go beyond approach and completely classify simulation ability Hamiltonians by their complexity classes. We do deriving necessary sufficient complexity-theoretic conditions characterizing...
Significance In this work we construct simple examples of 2D quantum spin-lattice models with small ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo>≤</mml:mo> </mml:math> 10) local state spaces which exhibit very unusual finite-size effects that term “size-driven phase transitions”: For all system sizes smaller than some threshold N , the low-energy physics are classical; for larger exhibits topological order. Most many-body too complex to be solved...
Abstract Recent work has characterized rigorously what it means for one quantum system to simulate another and demonstrated the existence of universal Hamiltonians—simple spin lattice Hamiltonians that can replicate entire physics any other many-body system. Previous universality results have required proofs involving complicated ‘chains’ perturbative ‘gadgets.’ In this paper, we derive a significantly simpler more powerful method proving Hamiltonians, directly leveraging ability encode...
A key challenge in realizing fault-tolerant quantum computers is circuit optimization. Focusing on the most expensive gates computation (namely, T gates), we address problem of T-count optimization, i.e., minimizing number that are needed to implement a given circuit. To achieve this, develop AlphaTensor-Quantum, method based deep reinforcement learning exploits relationship between optimizing and tensor decomposition. Unlike existing methods for AlphaTensor-Quantum can incorporate...
The phase diagram of a material is central importance to describe the properties and behaviour condensed matter system. We prove that general task determining quantum many-body Hamiltonian uncomputable, by explicitly constructing one-parameter family Hamiltonians for which this case. This work builds off recent results from Cubitt et al. Bausch al., proving undecidability spectral gap problem. However, in all previous constructions, was necessarily discontinuous function its parameters,...
We address two sets of long-standing open questions in linear algebra and probability theory, from a computational complexity perspective: stochastic matrix divisibility, divisibility decomposability distributions. prove that finite matrices is an NP-complete problem, extend this result to nonnegative matrices, completely-positive trace-preserving maps, i.e. the quantum analogue matrices. further hierarchy for distributions, showing distribution P, but NP-hard. For former, we give explicit...
Probabilistic language models, e.g. those based on an LSTM, often face the problem of finding a high probability prediction from sequence random variables over set tokens. This is commonly addressed using form greedy decoding such as beam search, where limited number highest-likelihood paths (the width) decoder are kept, and at end maximum-likelihood path chosen. In this work, we construct quantum algorithm to find globally optimal parse (i.e. for infinite with constant success probability....