Platforms & hybrid computing
Established businesses fund long research cycles. Their share prices also reflect many activities unrelated to quantum.
IBM · GOOGL · MSFT · AMZN · NVDAPORTFOLIO / EXPERIMENT 02
Twelve public companies. Three routes into quantum computing. One transparent basket to test whether technical progress becomes shareholder value.
THE ALLOCATION
Established businesses fund long research cycles. Their share prices also reflect many activities unrelated to quantum.
IBM · GOOGL · MSFT · AMZN · NVDAExposure to the tools required by multiple hardware architectures, rather than a single winning qubit design.
KEYS · FORMDirect exposure to technical progress, capped because losses, dilution and technology risk can dominate the outcome.
IONQ · QNT · QBTS · INFQ · RGTIThese are research allocations, not fair-value estimates. Quantum exposure is only part of the diversified companies’ businesses. The portfolio can move with AI, cloud, semiconductor and broad equity markets even when quantum itself has not changed.
PERFORMANCE / SAME START, SAME CURRENCY
This portfolio was created on 2026-10-31, using the previous completed close as a reference. There is one observation and no earned return yet. The first possible performance date is the next trading session. This is not a backtest or a claim of an executed purchase.
Dividends are included once through adjusted-price ratios; splits use the same adjustment basis at both endpoints. This gross research comparison excludes commissions, spreads, taxes and management fees. It is not executable NAV.
THE COMPANIES / ALLOCATION FROZEN 2026-10-31
Superconducting systems, error correction and enterprise access.
The largest allocation anchors the basket in a funded full-stack programme and established enterprise distribution.
Logical error rates, delivered roadmap milestones and paid use; distinguish targets from delivered systems.
Google Quantum AI and superconducting error-correction research.
A second major hardware programme reduces reliance on IBM; commercial quantum revenue remains uncertain.
Independent validation of error correction and economically useful workloads.
Azure Quantum, developer access and quantum research.
Cloud distribution offers exposure across hardware suppliers. The weight does not assume that any one hardware claim succeeds.
Repeatable logical-qubit results and customer adoption through Azure.
Amazon Braket provides access to multiple quantum technologies.
A diversified distribution layer with exposure to demand across vendors, supported by the broader AWS business.
Paid workloads and whether quantum delivers an advantage over classical alternatives.
GPU acceleration, simulation and hybrid quantum-classical integration.
Classical compute is needed around quantum systems. This allocation captures the integration layer without assuming a winning qubit.
Commercial deployments of hybrid workflows; separate quantum demand from general AI revenue.
Quantum control, measurement and test systems.
The larger equipment allocation backs measurement and control needs across architectures, with an established test business.
Disclosed quantum orders, adoption and margins; avoid treating all test revenue as quantum revenue.
Cryogenic probing and semiconductor testing for quantum development.
A smaller allocation adds laboratory infrastructure exposure, while recognising that mainstream semiconductor testing drives much of the business.
Cryogenic-system demand and repeat orders rather than the size of the overall semiconductor market.
Trapped-ion quantum computing and networking.
The largest specialist position, but still below one tenth of the portfolio because commercial scale and valuation remain uncertain.
Organic revenue, cash consumption, dilution, backlog conversion and reproducible performance.
Trapped-ion systems, quantum software and error correction.
A second trapped-ion platform broadens execution exposure. The new listing and operating losses justify a smaller position.
Cash runway, logical fidelity, paid deployments and delivery of hardware targets.
Quantum annealing and gate-model development.
A small allocation tests near-term optimisation demand. Annealing success must not be equated with universal fault-tolerant computing.
Repeat paying customers, workload economics, cash burn and separation of annealing and gate-model results.
Neutral-atom computing and quantum sensing.
Small exposure to another architecture and sensing markets; accounting, execution and new-listing risks limit the weight.
Computing versus sensing revenue, contract cash receipts and financial reporting quality.
Superconducting quantum processors and systems.
A deliberately small alternative to the larger superconducting programmes, given funding and execution risk.
System fidelity, contracted revenue, cash runway and share issuance.
QNT is Quantinuum equity, not a cryptocurrency. INFQ is Infleqtion equity. Security names and USD currency are checked before a quote enters the portfolio. Company sources establish involvement in quantum; their technical and commercial claims remain claims to test.
THE NEXT DECISION
Track logical error rates, independently repeatable results and workload economics versus the best classical alternative. A larger physical-qubit count alone is insufficient.
Track repeat customers, organic revenue, gross margins, cash runway and diluted shares. Partnerships and bookings are not equivalent to collected revenue.
Review after quarterly results or a material technical, funding or reporting event. Daily prices can change weights through drift; they do not trigger automatic trades.
No rebalance has occurred. A future change needs a published rationale, old and new weights, and a prospective effective close. The current experiment remains buy-and-hold until then. There is no automated trading or scheduled refresh; this snapshot was updated on request.
METHOD & LIMITS
At the baseline, each company receives its stated weight of $10,000.00. For any later session its value equals that initial amount multiplied by adjusted close on the session divided by adjusted close on the baseline, both taken from the same downloaded history. Values are summed across companies; each benchmark follows the same rule. Dividends are treated as reinvested in the same security, not distributed across the basket.
Fractional share equivalents are permitted. They describe the baseline only; a reinvested position may acquire more shares later. No leverage, shorting, contribution or rebalance is modelled. The dates follow SPY’s trading-session calendar. All holdings and benchmarks must cover every displayed session; no missing price is silently carried forward.
QQQ and SPY are broad equity comparisons, not risk-matched quantum benchmarks. A 20% specialist allocation can lose most of its value; the other 80% is still equity risk. Weights reflect a research judgement, not a valuation-based recommendation.
Last source fetch: 2026-10-31T09:38:28.064Z. Yahoo Finance daily closes and adjusted closes are research data, not an execution feed. All figures use the last date shared by every holding and benchmark. Source snapshots and calculation hashes are included in the downloadable data.
| Security | Initial weight | Current weight | Latest source close | Source |
|---|---|---|---|---|
| IBM | 20% | 20.00% | 2026-10-30 | Price history ↗ |
| GOOGL | 15% | 15.00% | 2026-10-30 | Price history ↗ |
| MSFT | 10% | 10.00% | 2026-10-30 | Price history ↗ |
| AMZN | 10% | 10.00% | 2026-10-30 | Price history ↗ |
| NVDA | 10% | 10.00% | 2026-10-30 | Price history ↗ |
| KEYS | 10% | 10.00% | 2026-10-30 | Price history ↗ |
| FORM | 5% | 5.00% | 2026-10-30 | Price history ↗ |
| IONQ | 8% | 8.00% | 2026-10-30 | Price history ↗ |
| QNT | 5% | 5.00% | 2026-10-30 | Price history ↗ |
| QBTS | 3% | 3.00% | 2026-10-30 | Price history ↗ |
| INFQ | 2% | 2.00% | 2026-10-30 | Price history ↗ |
| RGTI | 2% | 2.00% | 2026-10-30 | Price history ↗ |
Compare the original portfolio with two defensive candidates. Then explore a virtual account — real trading stays locked.