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Optimal compilation of parametrised quantum circuits
Get PDFParametrised quantum circuits contain phase gates whose phase is determined by a classical algorithm prior to running the circuit on a quantum device. Such circuits are used in variational algorithms like QAOA and VQE. In order for these algorithms to be as efficient as possible it is important that we use the fewest number of parameters. We show that, while the general problem of minimising the number of parameters is NP-hard, when we restrict to circuits that are Clifford apart from parametrised phase gates and where each parameter is used just once, we can efficiently find the optimal parameter count. We show that when parameter transformations are required to be sufficiently well-behaved, the only rewrites that reduce parameters correspond to simple `fusions'. Using this we find that a previous circuit optimisation strategy by some of the authors [Kissinger, van de Wetering. PRA (2019)] finds the optimal number of parameters. Our proof uses the ZX-calculus. We also prove that the standard rewrite rules of the ZX-calculus suffice to prove any equality between parametrised Clifford circuits
Towards experimental demonstration of quantum position verification using single photons
Get PDFThe geographical position can be a good credential for authentication of a party. This is the basis of position-based cryptography—but classically this cannot be done securely without physical exchange of a private key. Recently it has been shown that by combining quantum mechanics with the speed-of-light limit of special relativity, this might be possible: quantum position verification (QPV). Here we demonstrate experimentally a protocol that uses two-photon Hong-Ou-Mandel interference at a beamsplitter, which, in combination with two additional beam splitters and four detectors is rendering the protocol resilient to loss. With this, we are able to show first results towards an experimental demonstration of QPV
Classically simulating intermediate-scale instantaneous quantum polynomial circuits through a random graph approach
No full textQuantum advantage is a demonstration of a computation by a quantum computer that cannot be performed by the best classical computer in a reasonable time. A well-studied approach to demonstrating this on near-term quantum computers is to use random circuit sampling. It has been suggested that a good candidate for demonstrating quantum advantage with random circuit sampling is to use instantaneous quantum polynomial (IQP) circuits. These are quantum circuits where the unitary it implements is diagonal. In this paper, we introduce improved techniques for exactly classically simulating random IQP circuits. We find a simple algorithm to calculate an amplitude of an -qubit IQP circuit with dense random two-qubit interactions in time , which for sparse circuits (where each qubit interacts with other qubits) runs in for any given polynomial. Using a more complicated stabilizer decomposition approach we improve the algorithm for dense circuits to where . We further discuss how our techniques also lead to improved simulation times for IQP circuits on restricted architectures. We benchmarked our main algorithm and found that we can simulate up to 50-qubit circuits in a couple of minutes on a laptop, with 68-qubit sparse circuits taking a couple of hours. We estimate dense 70-qubit circuits are in range for large computing clusters
Algorithm configuration in sequential decision-making
Get PDFProper parameter configuration of algorithms is essential, but often time-consuming and complex, as many parameters need to be tuned simultaneously and evaluation can be expensive. In this paper, we focus on sequential decision-making (SDM) algorithms, which are applied to problems that require a series of decisions to be taken sequentially, aiming for an optimal cumulative outcome for the agent. To do this, every time the agent needs to make a decision, SDM algorithms take the current state of the environment as input and provide a decision as output. We propose a taxonomy of algorithm configuration approaches for SDM and introduce the concept of Per-State Algorithm Configuration (PSAC). To perform PSAC automatically, we present a framework based on Reinforcement Learning (RL). We demonstrate how PSAC by RL works in practice by applying it to two SDM algorithms on two SDM problems: Monte Carlo Tree Search, to solve a collaborative order picking problem in warehouses, and AlphaZero, to play a classic board game called Connect Four. Our experiments show that, in both use cases, PSAC achieves significant performance improvements compared to fixed parameter configurations. In general, our work expands the field of automated algorithm configuration and opens new possibilities for further research on SDM algorithms and their applications. Code is available at: https://github.com/ai-for-decision-making-tue/Per-State_Algorithm_Configuration
Testing and learning structured quantum Hamiltonians
Get PDFWe consider the problems of testing and learning an unknown -qubit quantum Hamiltonian expressed in its Pauli basis, from queries to its evolution operator under the normalized Frobenius norm. To this end, we prove the following results (with and without quantum memory) for Hamiltonians whose Pauli spectrum involves only -local terms or has sparsity at most :
(1) Local Hamiltonians: We give a tolerant testing protocol to decide if a Hamiltonian is -close to -local or -far from -local, with queries, thereby solving two open questions posed in a recent work by Bluhm, Caro and Oufkir [BCO'24]. For learning a -local Hamiltonian up to error , we give a protocol with query complexity and total time evolution . Our algorithm leverages the non-commutative Bohnenblust-Hille inequality in order to get a complexity independent of .
(2) Sparse Hamiltonians: We give a protocol for testing whether a Hamiltonian is -close to being -sparse or -far from being -sparse, with queries. For learning up to error , we show that queries suffices.
(3) Learning without quantum memory: The learning results stated above have no dependence on the system size , but require -qubit quantum memory. We give subroutines that allow us to reproduce all the above learning results without quantum memory; increasing the query complexity by a (log)-factor in the local case and an -factor in the sparse case.
(4) Testing without quantum memory: We give a new subroutine called Pauli hashing, which allows one to tolerantly test -sparse Hamiltonians using query complexity. A key ingredient is showing that -sparse Pauli channels can be tested in a tolerant fashion as being -close to being -sparse or -far under the diamond norm, using queries via Pauli hashing.
In order to prove these results, we prove new structural theorems for local Hamiltonians, sparse Pauli channels and sparse Hamiltonians. We complement our learning algorithms with lower bounds that are polynomially weaker. Furthermore, our algorithms use short time evolutions and do not assume prior knowledge of the terms on which the Pauli spectrum is supported on, i.e., we do not require prior knowledge about the support of the Hamiltonian terms
Challenging futures: using chatbots to reflect on aging and dementia
Get PDFIntertemporal reflection, flexibly thinking forward and backward in time, is vital for one's future planning. Yet, cultivating intertemporal reflection about encountering difficult futures, e.g., developing a progressive cognitive condition like dementia, can be challenging. We assessed people's attitudes towards dementia following conversing with a chatbot presented as either neurotypical or simulating dementia symptoms. While neither the chatbot's presentation nor the framing of participants' future selves impacted attitudes toward dementia, it influenced participants' experiences. When framed as future selves, the chatbot evoked a strong emotional connection, leading to reflection on aging, particularly with the chatbot simulating dementia symptoms. Participants interacting with the chatbot framed as a stranger with simulated symptoms often felt frustrated, especially when they had a task-oriented mindset.Chatbots can be promising tools for prompting reflections on challenging futures, such as dementia, although their effectiveness varies due to the tensions between simulated cognitive decline and expectations for effective communication
Joint reconstruction of multiple initial pressures and the speed of sound in photoacoustic tomography
Get PDFThe aim of the photoacoustic tomography (PAT) inverse problem is to reconstruct the initial pressure distribution from measured ultrasound waves generated by absorption of externally induced pulse of near-infrared light. Image reconstruction calls for modelling of these acoustic pressure waves, and thus knowledge of the speed of sound distribution of the target is required. However, the speed of sound is often unknown in practical situations, and therefore it would be valuable to reconstruct it together with the initial pressure. In addition, the speed of sound can provide interesting quantitative information of the imaged target. In this work, joint reconstruction of the initial pressure and speed of sound in PAT is studied. We propose an approach where photoacoustic measurements are performed using multiple different initial pressure distributions that are generated to the target using illuminations from different directions. Methodology for joint reconstruction of these multiple initial pressure and speed of sound distributions is formulated. The methodogy was evaluated with numerical simulations. The results show that, utilising data generated by producing multiple initial pressures in the target, initial pressure distributions can be reconstructed more accurately with less artefacts compared to a reference approach of utilising a single light illumination
Communicating through avatars in Industry 5.0: A focus group study on human-robot collaboration
Get PDFThe integration of collaborative robots (cobots) in industrial settings raises concerns about worker well-being, particularly due to reduced social interactions. Avatars - designed to facilitate worker interactions and engagement - are promising solutions to enhance the human-robot collaboration (HRC) experience. However, real-world perspectives on avatar-supported HRC remain unexplored. To address this gap, we conducted a focus group study with employees from a German manufacturing company that uses cobots. Before the discussion, participants engaged with a scripted, industry-like HRC demo in a lab setting. This qualitative approach provided valuable insights into the avatar’s potential roles, improvements to its behavior, and practical considerations for deploying them in industrial workcells. Our findings also emphasize the importance of personalized communication and task assistance. Although our study’s limitations restrict its generalizability, it serves as an initial step in recognizing the potential of adaptive, context-aware avatar interactions in real-world industrial environments
On the impossibility of actively secure distributed samplers
Get PDFOne-round secure computation is generally believed impossible due to the residual function attack: any honest-but-curious participant can replay the protocol in their head changing their input, and learn, in this way, a new output. Inputless functionalities are among the few that are immune to this problem. This paper studies one-round, multi-party computation protocols (MPC) that implement the most natural inputless functionality: one that generates a random sample from a fixed distribution. These are called distributed samplers. At Eurocrypt 2022, Abram, Scholl and Yakoubov showed how to build this primitive in the semi-honest model with dishonest majority. In this work, we give a lower bound for constructing distributed samplers with a malicious adversary in the standard model. More in detail, we show that for any construction in the stand-alone model with black-box simulation, even with a CRS and honest majority, the output of the sampling protocol must have low entropy. This essentially implies that this type of construction is useless in applications. Our proof is based on an entropic argument, drawing a new connection between computationally secure MPC, information theory and learning theory