Quantum Technologies – Hardware Designs – Part 2 of a 2 part series
An overview of IBM NightHawk , IONQ TEMPO, and D-Wave Advantage2 quantum systems.
In this post, the second of 2 exploring Quantum hardware systems (see part 1 here), we explore hardware designs being developed and commercialized to bring Quantum Computing to the masses!
Let’s get started!
Starting Point - Recall what is a Qubit?
As detailed in this Quantum Tech Overview Article, the basic unit of information in quantum computing is the qubit, or quantum bit. Qubits are essentially the quantum equivalent of the traditional bit used by classical computers to encode information. What makes qubits special is they can behave like a bit and store either a zero or a one, or a qubit can also be a weighted combination of zero and one at the same time (called superposition) which makes the scope of new computational possibilities massive.
IBM – NightHawk
IBM has been a long time player in the Quantum space – having been researching quantum computing since the 1970s. The company regularly publishes a comprehensive roadmap with milestones it is working to achieve. IBM introduced Quantum System One in 2019, the world’s first integrated quantum computing system designed for commercial use. This 20-qubit system marked a significant step toward making quantum computers viable for business applications.
Most recently, in late 2025 IBM announced its most advanced processor yet – NightHawk and indicated it should be available to users via the cloud and in on site systems by the end of 2026. Nighthawk delivers 120 superconducting transmon qubits in a square lattice connected by 218 tunable couplers, a 20% jump in connectivity over its predecessor, Heron. This layout boosts entanglement and circuit depth without spiking errors, supporting up to 5,000 two-qubit gates.
IBM fabricates transmon qubits on silicon substrates using superconducting materials like niobium or tantalum, with lithographic techniques compatible with existing semiconductor processes, including recent 300mm wafer fabs.
Transmon Qubits – these are tiny superconducting electrical circuits that behave like an artificial atom and can store a quantum bit of information. They are formed with two superconductors separated by a very thin insulator, which acts like a special, nonlinear inductor – called a Josephson junction.
The chip is cooled to extremely low temperatures (around a few thousandths of a degree above absolute zero) so the metal becomes superconducting and quantum effects dominate. Transmon qubits are a successful and widely adopted qubit design in superconducting quantum computing
Tunable couplers are specialized devices in superconducting quantum processors, that control interactions between qubits on demand, supporting qubits to connect precisely when needed for computations, then disconnect to prevent unwanted interference. Nighthawk supports cloud access to NightHawk with more than 100-qubit processors and integrates with IBM’s Qiskit software for algorithm development.
Oiskit Software Stack
Qiskit is an open-source Python-based software stack for quantum computing supporting users to create, optimize, and execute quantum programs on real hardware or simulators. Oiskit implements a modular, extensible architecture with core components like circuit builders, transpilers, primitives, and optimizers, allowing scalable workflows from high-level abstractions to low-level gates and it supports backend-agnostic execution across providers like IBM, IONQ, and AWS.
Qiskit provides tools for building quantum circuits with gates and libraries, simulating executions, and running on cloud hardware via Qiskit Runtime. It includes AI-powered code assistance, a Functions Catalog for pre-built algorithms in optimization and chemistry, and Serverless for hybrid quantum-classical supercomputing. It also includes visualization, benchmarking via Benchpress, and ecosystem extensions.
Core Technologies
Qubit type: 120 superconducting transmon qubits fabricated on silicon substrates
Operating conditions: requires cryogenic cooling to near absolute zero temperatures in dilution refrigerators
Control and readout: leverages FPGA-based electronics for pulse generation, with real-time error decoding under 480 nanoseconds
Coherence times: achieved over 400 microseconds
IBM has plans to scale Nighthawk to deliver up to 7,500 gates by the end of 2026, followed by up to 10,000 two-qubit gates in 2027, and by 2028 potentially up to 15,000 gates.
IBM’s Hybrid Model for Quantum+Classic Computing: Quantum-Centric Supercomputing (QCSC)
Leaving no stone unturned, IBM clearly understands that Quantum Computing will exist in a world now dominated by classic 1’s and 0’s based computing. In March 2026, IBM published a reference architecture or blueprint (no pun intended 😊 ) to illustrate how it may be possible to bring quantum and classic computing together to run workloads in what IBM calls Quantum-Centric Supercomputing.
The three layer architecture maps a base hardware infrastructure. The base is the quantum system, with one or more interconnected Quantum Processing Units (QPU), a classic compute runtime using specialized classical FPGAs, ASICs, and CPUs whose job is to enable QPU operations from error correction coding to qubit calibration to active qubit reset. The second tier includes programmable CPU and GPU systems that are co-located with the quantum system and connected via a low-latency, near-time interconnects. The upper layer is the orchestration layer that includes the Quantum Resource Management Interface (QRMI), an open source library that abstracts away hardware-specific details and delivers APIs for quantum resource acquisition, task running, and systems monitoring.
IONQ TEMPO - Trapped Ions
IONQ employs a trapped-ion approach, which is claimed to represent the most accurate quantum computing technology currently available, and it is implemented at room temperature. IONQ deploys barium ions as qubits - ionized atoms held in linear chains within custom ion traps. These traps use radiofrequency (RF) electrodes to suspend ions in an ultra-high vacuum for isolation from environmental noise.
Trapped-ion systems have become one of the established platforms for advancing quantum technology. These systems use electric fields to trap and move ions in a quantum processor, as well as lasers to manipulate their atomic and motional quantum states. This architecture supports the deployment of long chains of interconnected qubits that remain in a state of quantum coherence for long periods of time.
Qubits are stored in stable electronic states of each ion, and quantum information can be transferred through the collective quantized motion of the ions in a shared trap. Lasers are applied to induce coupling between the qubit states (for single qubit operations) or coupling between the internal qubit states and the external motional states for entanglement between qubits.
Like classical CPUs, the size of IONQ’s individual Quantum Processing Unit (QPU) is limited by the high-fidelity entangling gates that need to be deployed over the length of the chain which creates a practical size limit for IONQ’s QPUs – aka at some point connecting the qubits becomes too complex. To address the limitation IONQ is working to build multicore QPUs, that place multiple processing cores on one chip similar to how multicore CPUs used in classic computing.
Multiple ion chains are manipulated to dynamically form quantum computing cores – essentially the computing cores can be reconfigured to ensure the system utilizes the full computational capabilities of all the qubits in the system, thereby increasing the computational power exponentially.
Core Technologies
Qubit type: supports 100 qubit trapped-ion quantum computing in a single ion chain with all-to-all connectivity, and it leverages individual barium ions
Operating conditions: operates at room temperature
Control and readout: control systems leverage precisely calibrated lasers: individual beams for single-qubit rotations and global beams for entangling gates via ion chain vibrations
Coherence times: IONQ claims industry-leading coherence: T1 of 10-100 seconds and T2 (phase coherence) around 1 second
IONQ’s approach is differentiated by its long coherence times and all-to-all qubit connectivity. We can expect IONQ to be a leader in the Quantum Computing field going forward.
D-Wave Advantage2 – Quantum Annealing
D-Wave utilizes Quantum Annealing running adiabatic quantum computing algorithms - a system optimized for solving complex optimization and sampling problems by finding low-energy states in defined energy landscapes.
Note: D-Wave provides annealing and gate-model quantum systems and related services. Here we explore its Quantum Annealing technologies only.
The system uses superconducting fluxonium qubits in loops, where qubits start in superposition and evolve through a programmable Hamiltonian that gradually introduces problem-specific biases and couplings. Couplers between qubits enable entanglement, allowing correlated states (same or opposite) to influence outcomes. D-Wave’s Advantage2 system deploys more than 4,400 qubits in a Zephyr topology with 20-way connectivity. Qubits are oriented vertically or horizontally and use shifted connections via three coupler types with each qubit connecting to 20 others—sixteen via internal couplers plus two external and two odd couplers to aligned qubits.
Quantum Annealing is essentially a computational process used to find optimal solutions to complex problems. It is a method of computation inspired by the principles of quantum physics and operates on the concept of “annealing,” a heating and cooling process traditionally applied to metals. Quantum Annealing uses quantum effects instead of thermal energy.
It explores multiple possible solutions simultaneously through quantum effects, potentially finding high-quality answers to certain optimization problems more efficiently than traditional computing methods. It has the potential to handle problems of scale and complexity beyond traditional computing capabilities.
The Annealing Process
At the start of the annealing process, the qubit is in a superposition state, which can be represented as a single valley with a single minimum energy. As the Quantum Annealing process runs, an energy barrier is raised which separates the single minimum energy into two valleys – also called a double-well potential.
As the system evolves, the potential evolves into a double-well shape, meaning that at the end of the anneal cycle, the qubit can end up in one of the two valley states. The problem encoding controls the outcome of this process: the magnetic field bias will tilt the potential, and the couplers allow interaction between qubits in a way that leads to correlation of their measurement outcomes. By the end of the anneal cycle, qubits are in a state that ideally represents the minimum energy state of the encoded problem.
In Quantum Annealing, tunneling enables qubits to escape local energy minima—shallow traps or valleys—and reach the global minimum by “tunneling” through barriers between states. Local energy minima in Quantum Annealing refers to suboptimal states in the energy landscape where the system’s energy is lower than nearby configurations but higher than the global minimum.
Quantum Annealing leverages quantum tunneling to navigate between local minima toward lower-energy states. Quantum tunneling occurs when a particle passes through a potential energy barrier even if it lacks the classical energy to climb over it, due to its wave-like nature in quantum mechanics. Unlike a classical ball that bounces off a hill, the particle’s probability wave extends through the barrier, decaying exponentially but retaining a non-zero chance of emerging on the other side.
Adiabatic Quantum Computing
Adiabatic quantum computing algorithms rely on the adiabatic theorem of quantum mechanics to evolve a system’s ground state toward a solution. The adiabatic theorem states that a quantum system remains in its ground state if the Hamiltonian H(t) changes slowly enough compared to the energy gap between the ground and first excited states.
In practice, the system starts with a simple initial Hamiltonian shown as H_B, known as an easy ground state, and evolves to a problem Hamiltonian H_P whose ground state encodes the solution. Implementation steps are:
Encode the problem into H_P, where the ground state minimizes an energy function matching the objective.
Initialize in H_B’s known ground state.
Anneal: linearly interpolate H(s)=(1-s)H_B+sH_P for s from 0 to 1 over time T and measure the final state for the answer.
Hamiltonian
The Hamiltonian of a system represents the total energy of the system; that is, the sum of the kinetic and potential energies of all particles associated with the system. The Hamiltonian takes different forms and can be simplified in some cases by taking into account the concrete characteristics of the system under analysis, such as single or several particles in the system, interaction between particles, kind of potential energy, time varying potential or time independent one. A time-varying Hamiltonian gradually shifts from an initial “driver” Hamiltonian that promotes mixing of states to a final “problem” Hamiltonian encoding the specific optimization task, like minimizing energy in a landscape of valleys and peaks.
Core Technologies
Qubit type: ~ 4400 superconducting fluxonium qubits in loops
Operating conditions: requires cryogenic cooling and operates at a temperature below 20 mK
Control and readout: Control relies on multiplexed digital-to-analog converters (DACs). QPUs use several controls that are manipulated by individual on-QPU DACs.
Coherence times: relaxation times in excess of 100 microseconds
Quantum Annealing faces several real world challenges including:
Integration of quantum solutions with existing business systems and workflows - requires specialized expertise
Unclear ROI where currently most business applications are experimental, making it difficult to predict when ROI will occur
Problem formulation -translating actual business problems into formats suitable for quantum processing requires both quantum knowledge and deep domain expertise
While Quantum Annealing systems are still maturing, advancements in both theory and practical quantum infrastructure are bringing practical applications within reach such that Quantum Annealing could play a major role in revolutionizing many industries.
Wrapping Up
Quantum computing is a long-term endeavor that requires sustained funding and patience. Investors and governments are increasingly expecting measurable progress and clear timelines for return on investment. This creates tension between scientific uncertainty and business expectations. Over promising could risk disillusionment, while under promising may slow down investment support.
The brief overview of the three approaches outlined here shows this is not just about building quantum chips. Each company is betting on fundamentally different methodologies and technologies to solve the hardest problems in the quantum field. While great progress is being made, which of them will become an industry leading technology in practical hardware has yet to be determined.
If you missed it – Check Out Part 1 of this series here
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