Quantum Computing
Learn about quantum algorithms, quantum information theory, and quantum computing applications
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Wed, May 20
18 items found
Non-equilibrium quantum dynamics of interacting integrable models by Monte Carlo sampling Lehmann representations
Determining the dynamics of interacting integrable many-particle quantum systems at finite times after homogeneous quantum quenches is a long-standing challenge. We present a Monte Carlo sampling scheme that numerically evaluates the Lehmann representation for time-dependent expectation values of local operators, allowing us to access system sizes and times significantly beyond the reach of existing methods. The approach accommodates both the full Lehmann sum and the Quench Action formalism. We benchmark against exact results for non-interacting lattice and continuum models and short-time results at weak interactions, finding excellent agreement. We apply the method to quantum quenches from a Bose-Einstein condensate in the repulsive Lieb-Liniger model and determine the time evolution of the order parameter for a wide range of interaction strengths. We discuss the emergence of a "sign problem" for more general dynamical correlators and setups.
On Performance and Limitations of NISQ Hardware for Simulations of Quantum Wave Packet Dynamics
Digital quantum simulation offers a promising route for studying quantum dynamics, but efficient operator representations and circuit depth remain key challenges for near-term hardware. We investigate one-dimensional wave packet dynamics using a grid-based encoding of the wave function onto qubit registers. Time evolution is implemented via split-operator approach, with kinetic energy operator applied using Quantum Fourier Transform (QFT) with polynomial scaling and potential energy operator expressed through commuting Pauli-Z gates, improving accuracy and enabling incorporation of arbitrary discretized potentials. While the full Pauli decomposition of Hamiltonian scales exponentially as O(4^n ), the present approach reduces the operator scaling to O(2^n) for n qubits. We benchmark this approach on classical simulators and quantum hardware (IBM Quantum and IonQ) for two- to five-qubit implementations. For two- and three-qubit cases, all platforms qualitatively reproduce the benchmarked dynamics; at larger qubit counts, the IBM results deviate more strongly, whereas IonQ remains closer to the benchmark.
Mechanism of wavefunction collapse in measurements of separated quantum subsystems
The specific advance of this work is to propose a mechanism by which superpositions collapse during measurement of the separated subsystems of entangled quantum states. It is shown how the phase that locks together entangled states plays a special role in the measurement of isolated subsystems. This `contextual' phase is installed randomly into the entangled state, and decides the measurement outcomes for the subsystems by directing the collapse of each superposition to a particular classical outcome when a subsystem is measured. The measuring apparatus thus obtains a classical read-out of the quantum correlations embedded in an entangled state. More broadly, these results solidify the theory of measurement of quantum superpositions.
Quantum algorithm for Discrete Gaussian Sampling
Discrete Gaussian Sampling on lattices is a fundamental problem in lattice-based cryptography. It appears both in basic cryptographic primitives such as digital signatures and as an important cryptanalysis building block for solving hard lattice problems. In this paper, we show a quantum algorithm based on the quantum rejection sampling technique whose complexity is asymptotically quadratically faster than its classical counterpart in [Wang & Ling, IEEE Trans. Inf. Theory 2019]. Our sampler outputs a quantum state which can either be measured to get the desired distribution or be used directly as such in other quantum algorithms. By doing so, we derive two versions of quantum dual attacks that improve upon the previous ones in [Pouly & Shen, EUROCRYPT 2024]. The two versions are incomparable, each having distinct advantages (speed vs memory requirement). The second version is particularly interesting as it requires only polynomial classical and quantum memory, excluding the classical memory used in the preprocessing step of the Discrete Gaussian sampler. Our quantum Discrete Gaussian sampler can also be used to speed up the algorithm for solving the Short Integer Solution problem, in any norm, of [Bollauf, Pouly & Shen, ePrint 2026/225].
Quantum eMotion and JMEM TEK Execute Consortium Agreement for Hardware Root-of-Trust SoC Development
Quantum eMotion Corp. (QeM) and Taiwan-based secure semiconductor designer JMEM TEK have executed an international project consortium agreement to develop a quantum-resilient Universal Security System-on-Chip (SoC). The contract solidifies a memorandum of understanding signed in September 2025 and transitions the partnership into a structured R&D phase under the Canada–Taiwan 2024–25 Collaborative R&D Program (CIIP). The [...] The post Quantum eMotion and JMEM TEK Execute Consortium ...
Toshiba Europe and Quantum Bridge Technologies Demonstrate Global Information-Theoretic Network Architecture
Toshiba Europe Limited (Toshiba) and Quantum Bridge Technologies Inc. (QBT) have finalized a live network demonstration establishing an international, information-theoretic secure (ITS) data transmission system. Unveiled at the Optical Fiber Communication Conference (OFC) 2026, the cross-continental deployment connected operational metropolitan Quantum Key Distribution (QKD) networks in Cambridge, UK, and Toronto, Canada. The physical implementation was [...] The post Toshiba Europe and Quan...
Italtel and Quantum Bridge Technologies Form Strategic International Post-Quantum Security Partnership
Italtel, an Italian multinational system integrator, has entered into a strategic international partnership with Canadian cybersecurity developer Quantum Bridge Technologies Inc. (QBT). Formally established at the Embassy of Canada in Rome, the joint initiative focuses on the commercial deployment of quantum-safe network architectures to secure sensitive communication systems against advanced cryptanalytic threats. The collaboration combines [...] The post Italtel and Quantum Bridge Technolo...
Quantinuum and Synopsys Partner to Integrate Quantum Algorithms into Engineering Simulation Workflows
Quantinuum has entered into a strategic collaboration with electronic design automation (EDA) and engineering simulation developer Synopsys, Inc. The joint initiative focuses on integrating quantum computing natively into standard industrial engineering software and libraries. The partnership addresses the computational bottlenecks of classical high-performance computing (HPC) when processing large-scale modeling workloads. The programmatic objective is to [...] The post Quantinuum and Synop...
Superpositions Launches Cloud-Based Quantum Software Ecosystem and Automated Use-Case Library
Quantum computing startup Superpositions has launched its integrated product ecosystem and open-access Quantum Solutions Library to streamline the operationalization of industrial quantum-hybrid software. The platform addresses standard bottlenecks in corporate adoption, such as restricted multi-vendor hardware access, inadequate benchmark auditing, and the engineering overhead associated with mapping enterprise data to noisy intermediate-scale quantum (NISQ) devices. [...] The post Superpos...
PacketLight and Quantum XChange Partner to Deliver Crypto-Agile Optical Transport Solutions
PacketLight Networks has formed a strategic technical partnership with post-quantum cyber defense developer Quantum XChange to integrate advanced post-quantum cryptography (PQC) into its optical transport hardware portfolio. The joint initiative expands PacketLight Networks’ existing FIPS-certified Layer-1 encryption and Quantum Key Distribution (QKD) framework by incorporating National Institute of Standards and Technology (NIST) post-quantum cryptographic standards. [...] The post PacketLi...
Kipu Quantum Launches Hybrid Framework to Enable Offline Inference for Quantum Machine Learning
Kipu Quantum has released an off-line Digitized Quantum Feature Extraction (DQFE) pipeline that allows quantum-enhanced machine learning models to execute inference operations entirely on classical hardware. The architecture separates the quantum and classical processing loops, restricting quantum processor utilization to an initial, specialized training stage. By eliminating real-time Quantum Processing Unit (QPU) dependencies during active [...] The post Kipu Quantum Launches Hybrid Framew...