Quantum AI Report

The convergence of Quantum with AI

Quantum Machines

Quantum Machines provides hardware-agnostic control systems for quantum computers, including the Quantum Orchestration Platform for pulse-level control and readout. Its technology is widely adopted by quantum hardware developers for real-time control and error correction.

AI-written profile · not yet reviewed · 1 September 2026

Headquarters
Tel Aviv, Israel
Founded
2018
Status
Private
cryogenics control

Investors Red Dot Capital, TLV Partners, Battery Ventures

Coverage

Quantum Computing Report

FirstQFM Foundation Model Cuts Quantum Gate Times by 33% on QuantWare QPU via NVIDIA Computing

FirstQFM AB reported a 33% reduction in single-qubit gate times on QuantWare's 21-qubit Contralto-D21 superconducting processor while maintaining gate fidelity. The result used FirstQFM's quantum foundation model, integrated with Quantum Machines' OPX control hardware and QUA calibration stack. The model was developed with NVIDIA compute resources.

OutlookPlausible

If FirstQFM's model generalizes beyond the Contralto-D21, it could be embedded in Quantum Machines' QUA calibration stack to automate per-device gate-time optimization, shrinking tune-up time for new QuantWare chips.

algorithms softwarecryogenics controlsuperconductingFirstQFM ABNVIDIAQuantWareQuantum Machines
Quantum Zeitgeist

AMD chips help Quantum Machines control qubits with nanosecond precision

Quantum Machines is focused on the classical computing layer that must issue qubit control signals on nanosecond timescales before quantum states decohere. AMD hardware is being used to reduce latency between the nanosecond-scale control operations and the millisecond-scale classical orchestration around them. The work is framed as support for the many simultaneous classical computations a quantum computer requires.

OutlookPlausible

If AMD's latency characteristics hold in integrated deployments, Quantum Machines could make deterministic nanosecond-scale feedback practical across small multi-qubit systems, enabling faster closed-loop control for early error-correction experiments.

cryogenics controlAMDQuantum Machines
arXiv quant-ph

ML-Powered FPGA-based Real-Time Quantum State Discrimination Enabling Mid-circuit Measurements

A preprint on arXiv describes an FPGA-based machine-learning classifier designed to identify superconducting qubit states in real time. The work targets mid-circuit measurement and conditional feed-forward, framing current superconducting readout as both latency-bound and error-prone compared with classical transistor-level state detection. The abstract stops short of reporting full system-level benchmarks, presenting the integration as a response to that readout gap.

OutlookPlausible

If the FPGA implementation validates on current superconducting hardware with multi-qubit readout, it could be integrated into existing control stacks as a drop-in discriminator for MCM loops within two years.

superconductingcryogenics controlerror correctionGoogleIBMQuantum MachinesRigettiZurich Instruments