Jacob Biamonte
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View article: Improving Quantum Approximate Optimization by Noise-Directed Adaptive Remapping
Improving Quantum Approximate Optimization by Noise-Directed Adaptive Remapping Open
We present Noise-Directed Adaptive Remapping (NDAR), a heuristic algorithm for approximately solving binary optimization problems by leveraging certain types of noise. We consider access to a noisy quantum processor with dynamics that feat…
View article: Barren Plateaus in Variational Quantum Computing
Barren Plateaus in Variational Quantum Computing Open
Variational quantum computing offers a flexible computational paradigm with applications in diverse areas. However, a key obstacle to realizing their potential is the Barren Plateau (BP) phenomenon. When a model exhibits a BP, its paramete…
View article: Improving Quantum Approximate Optimization by Noise-Directed Adaptive Remapping
Improving Quantum Approximate Optimization by Noise-Directed Adaptive Remapping Open
We present Noise-Directed Adaptive Remapping (NDAR), a heuristic algorithm for approximately solving binary optimization problems by leveraging certain types of noise. We consider access to a noisy quantum processor with dynamics that feat…
View article: Physics for secure and efficient societies
Physics for secure and efficient societies Open
Chapter 7 presents an introduction and sections on: second quantum revolution: quantum computing and cybersecurity; sensors and their applications; the space sector: current and future prospects; large-scale complex sociotechnical systems …
View article: Computational phase transition signature in Gibbs sampling
Computational phase transition signature in Gibbs sampling Open
Gibbs sampling is fundamental to a wide range of computer algorithms. Such algorithms are set to be replaced by physics based processors—be it quantum or stochastic annealing devices—which embed problem instances and evolve a physical syst…
View article: On Commutative Penalty Functions in Parent-Hamiltonian Constructions
On Commutative Penalty Functions in Parent-Hamiltonian Constructions Open
There are several known techniques to construct a Hamiltonian with an expected value that is minimized uniquely by a given quantum state. Common approaches include the parent Hamiltonian construction from matrix product states, building ap…
View article: Tridiagonal matrix decomposition for Hamiltonian simulation on a quantum computer
Tridiagonal matrix decomposition for Hamiltonian simulation on a quantum computer Open
The construction of quantum circuits to simulate Hamiltonian evolution is central to many quantum algorithms. State-of-the-art circuits are based on oracles whose implementation is often omitted, and the complexity of the algorithm is esti…
View article: Mitigating Quantum Gate Errors for Variational Eigensolvers Using Hardware-Inspired Zero-Noise Extrapolation
Mitigating Quantum Gate Errors for Variational Eigensolvers Using Hardware-Inspired Zero-Noise Extrapolation Open
Variational quantum algorithms have emerged as a cornerstone of contemporary quantum algorithms research. Practical implementations of these algorithms, despite offering certain levels of robustness against systematic errors, show a declin…
View article: On Translation-Invariant Matrix Product States and advances in MPS representations of the $W$-state
On Translation-Invariant Matrix Product States and advances in MPS representations of the $W$-state Open
This work is devoted to the study Translation-Invariant (TI) Matrix Product State (MPS) representations of quantum states with periodic boundary conditions (PBC). We pursue two directions: we introduce new methods for constructing TI MPS r…
View article: Complex systems in the spotlight: next steps after the 2021 Nobel Prize in Physics
Complex systems in the spotlight: next steps after the 2021 Nobel Prize in Physics Open
The 2021 Nobel Prize in Physics recognized the fundamental role of complex systems in the natural sciences. In order to celebrate this milestone, this editorial presents the point of view of the editorial board of JPhys Complexity on the a…
View article: Robustness of Variational Quantum Algorithms against stochastic parameter perturbation
Robustness of Variational Quantum Algorithms against stochastic parameter perturbation Open
Variational quantum algorithms are tailored to perform within the constraints of current quantum devices, yet they are limited by performance-degrading errors. In this study, we consider a noise model that reflects realistic gate errors in…
View article: Circuit depth scaling for quantum approximate optimization
Circuit depth scaling for quantum approximate optimization Open
Variational quantum algorithms are the centerpiece of modern quantum programming. These algorithms involve training parameterized quantum circuits using a classical co-processor, an approach adapted partly from classical machine learning. …
View article: Tensor networks in machine learning
Tensor networks in machine learning Open
A tensor network is a type of decomposition used to express and approximate large arrays of data.A given dataset, quantum state, or higher-dimensional multilinear map is factored and approximated by a composition of smaller multilinear map…
View article: On Symmetric Pseudo-Boolean Functions: Factorization, Kernels and Applications
On Symmetric Pseudo-Boolean Functions: Factorization, Kernels and Applications Open
A symmetric pseudo-Boolean function is a map from Boolean tuples to real numbers which is invariant under input variable interchange. We prove that any such function can be equivalently expressed as a power series or factorized. The kernel…
View article: Ion-native variational ansatz for quantum approximate optimization
Ion-native variational ansatz for quantum approximate optimization Open
Variational quantum algorithms involve training parameterized quantum circuits using a classical co-processor. An important variational algorithm, designed for combinatorial optimization, is the quantum approximate optimization algorithm. …
View article: Quantum-machine-learning channel discrimination
Quantum-machine-learning channel discrimination Open
In the problem of quantum channel discrimination, one distinguishes between a given number of quantum channels, which is done by sending an input state through a channel and measuring the output state. This work studies applications of var…
View article: Progress towards Analytically Optimal Angles in Quantum Approximate Optimisation
Progress towards Analytically Optimal Angles in Quantum Approximate Optimisation Open
The quantum approximate optimisation algorithm is a p layer, time variable split operator method executed on a quantum processor and driven to convergence by classical outer-loop optimisation. The classical co-processor varies individual a…
View article: Milestones of research activity in quantum computing: EPS grand challenges
Milestones of research activity in quantum computing: EPS grand challenges Open
We argue that quantum computing underwent an inflection point circa 2017. Long promised funding materialised which prompted public and private investments around the world. Techniques from machine learning suddenly influenced central aspec…
View article: Tensor networks in machine learning
Tensor networks in machine learning Open
A tensor network is a type of decomposition used to express and approximate large arrays of data. A given data-set, quantum state or higher dimensional multi-linear map is factored and approximated by a composition of smaller multi-linear …
View article: On Circuit Depth Scaling For Quantum Approximate Optimization
On Circuit Depth Scaling For Quantum Approximate Optimization Open
Variational quantum algorithms are the centerpiece of modern quantum programming. These algorithms involve training parameterized quantum circuits using a classical co-processor, an approach adapted partly from classical machine learning. …
View article: Experimental quantum adversarial learning with programmable superconducting qubits
Experimental quantum adversarial learning with programmable superconducting qubits Open
Quantum computing promises to enhance machine learning and artificial intelligence. Different quantum algorithms have been proposed to improve a wide spectrum of machine learning tasks. Yet, recent theoretical works show that, similar to t…
View article: Training saturation in layerwise quantum approximate optimization
Training saturation in layerwise quantum approximate optimization Open
Quantum Approximate Optimisation (QAOA) is the most studied gate based variational quantum algorithm today. We train QAOA one layer at a time to maximize overlap with an $n$ qubit target state. Doing so we discovered that such training alw…
View article: Numerical hardware-efficient variational quantum simulation of a soliton solution
Numerical hardware-efficient variational quantum simulation of a soliton solution Open
Implementing variational quantum algorithms with noisy intermediate-scale quantum machines of up to a hundred qubits is nowadays considered as one of the most promising routes towards achieving a quantum practical advantage. In multiqubit …
View article: Parameter concentrations in quantum approximate optimization
Parameter concentrations in quantum approximate optimization Open
The quantum approximate optimization algorithm (QAOA) has become a\ncornerstone of contemporary quantum applications development. In QAOA, a\nquantum circuit is trained -- by repeatedly adjusting circuit parameters -- to\nsolve a problem. …
View article: Unraveling the effects of multiscale network entanglement on empirical systems
Unraveling the effects of multiscale network entanglement on empirical systems Open
View article: Benchmarking variational quantum simulation against an exact solution
Benchmarking variational quantum simulation against an exact solution Open
Implementing variational quantum algorithms with noisy intermediate-scale quantum machines of up to a hundred of qubits is nowadays considered as one of the most promising routes towards achieving quantum practical advantage. In multiqubit…
View article: Abrupt transitions in variational quantum circuit training
Abrupt transitions in variational quantum circuit training Open
Variational quantum algorithms dominate gate-based applications of modern\nquantum processors. The so called, {\\it layer-wise trainability conjecture}\nappears in various works throughout the variational quantum computing\nliterature. The…
View article: Universal variational quantum computation
Universal variational quantum computation Open
Variational quantum algorithms dominate contemporary gate-based quantum\nenhanced optimisation, eigenvalue estimation and machine learning. Here we\nestablish the quantum computational universality of variational quantum\ncomputation by de…
View article: Quantum Machine Learning Tensor Network States
Quantum Machine Learning Tensor Network States Open
Tensor network algorithms seek to minimize correlations to compress the classical data representing quantum states. Tensor network algorithms and similar tools—called tensor network methods—form the backbone of modern numerical methods use…
View article: Deep learning super-diffusion in multiplex networks
Deep learning super-diffusion in multiplex networks Open
Complex network theory has shown success in understanding the emergent and collective behavior of complex systems Newman 2010 Networks: An Introduction (Oxford: Oxford University Press). Many real-world complex systems were recently discov…